Long-Distance LIDAR Mapping Schematic for Fast Monitoring of Bioaerosol Pollution over Large City Areas

*Dimitar Stoyanov, Ivan Nedkov, Veneta Groudeva, Zara Cherkezova-Zheleva, Ivan Grigorov, Georgy Kolarov, Mihail Iliev, Ralitsa Ilieva, Daniela Paneva and Chavdar Ghelev*

## **Abstract**

Light detection and ranging (LIDAR) atmospheric sensing is a major tool for remote monitoring of aerosol pollution and its propagation in the atmosphere. Combining LIDAR sensing with ground-based aerosol monitoring can form the basis of integrated air-quality characterization. When present, biological atmospheric contamination is transported by aerosol particles of different size known as bioaerosol, whose monitoring is now among the basic areas of atmospheric research, especially in densely-populated large urban regions, where many bioaerosol-emitting sources exist. Thus, promptly identifying the bioaerosol sources, including their geographical coordinates, intensities, space-time distributions, etc., becomes a major task of a city monitoring system. This chapter argues in favor of integrating a LIDAR mapping schematic with in situ sampling and characterization of the bioaerosol in the urban area. The measurements, data processing, and decision-making aimed at preventing further atmospheric contamination should be performed in a near-real-time mode, which imposes certain demands on the typical LIDAR schematics, including long-range sensing as a critical parameter, especially over large areas (10 – 100 km2). In this chapter, we describe experiments using a LIDAR schematic allowing near-real-time long-distance measurements of urban bioaerosol combined with its ground-based sampling and physicochemical and biological studies.

**Keywords:** LIDAR monitoring , particulate matter, atmospheric pollution, contaminations

## **1. Introduction**

Atmospheric aerosol pollution or more appropriately particulate matter (PM) is key subject for the human health and ecosystem stability. At present, more than 2000 papers are published per year addressing research topics related to

atmospheric aerosols [1]. However, surprisingly little is known regarding the genesis, composition, or dynamics of the atmosphere's microbial inhabitants, particle's chemical composition, particle's surface pollution, and their relation with the PM [2, 3]. In what concerns human health, the most harmful ones are the particles with sizes below 10 μm, standardized in terms of permissible concentration as PM2.5 and PM10. The fast pace of the information technology development in the first decade of twenty-first century created extraordinary possibilities for organizing systems for real-time monitoring of the atmosphere over urban areas. The implementation of such techniques requires the development of modern fast sensor systems, whose principle of operation, size, and convenient management ensures a problem-free functioning of the information systems without affecting the life in the particular urban area. The well-known techniques of PM monitoring include as a rule a small number of stations and the use of specialized algorithms for rapid assessment of the aerosol pollution's spatial structure above the city. These approaches suffer from a number of drawbacks, such as limited spatial resolution as compared with the urban structure, the low temporal resolution from the viewpoint of the quick polluting processes, and following the PM proliferation, the use of non-calibrated devices, and the subjectivity in selecting the sampling sites. A major drawback of the techniques in question is the relatively slow determination of the pollution sources. Of particular interest in assessing the air quality in urban areas is the biological contamination transported by PM in the form of bioaerosols. According to [4], almost 25% of the total airborne PM above land surfaces contains biological materials in the form of pollens, fungal spores, bacteria, viruses, and fragments from plants, animals, or living organisms with a size ranging from 0.02 up to 100 μm [5–8].

The present chapter discusses the capabilities of remote techniques of analyzing the pollution fields in conjunction with in situ sampling of bioaerosols and investigation of PM pollution as one of the promising approaches to raising the efficiency of systems of air monitoring over urban areas [9–11]. The light detection and ranging (LIDAR) techniques are considered as being among the ones best suited to real-time scanning and/or long-term monitoring of the pollution above large areas. Their advantages arise from several factors: (1) light wavelengths commensurate with or close to the PM size, (2) good resolution in terms of distance (5–30 m) and elevation angle (~1o ), and (3) range of operation (20–30 km) [12]. In a scanning mode of operation, the LIDAR techniques allow one to construct LIDAR maps of the aerosol pollution distribution over large cities, which can be easily juxtaposed with the ground atmospheric monitoring networks. Special attention was paid here to the development of a specific LIDAR measurement schematic to be suitably combined with in situ bioaerosol sampling investigating its crystallo-chemical and surface structure, including the presence of bacteria and adsorbed nano- and micron-sized organic and inorganic contamination. The successful applicability of such combined methodology is demonstrated below by the results of a study of the РМ size distribution and genesis in two typical urban zones within a large city (Sofia, capital of Bulgaria, population of approx. 1.3 million), in places where the LIDAR monitoring had established previously a mass concentration in peak hours of vehicle traffic.

#### **1.1 LIDAR technologies applied in aerosol sensing**

In the literature [13] one can find descriptions of various LIDAR techniques based on the interaction of laser radiation with bioaerosols, such as Mie, Rayleigh,

**51**

*Long-Distance LIDAR Mapping Schematic for Fast Monitoring of Bioaerosol Pollution over Large…*

and Raman [9, 10] scattering and laser-induced fluorescence of bioaerosols [14]. Depolarization LIDAR techniques are also employed, whereby measuring the laser light depolarization, and based on models of non-spherical aerosols, the latter's origin can be distinguished, e.g., dust, smoke, pollen, including bioagents as anthrax,

The Mie scattering-based LIDAR techniques of bioaerosol remote sounding allow one to use simple and inexpensive LIDAR equipment efficient in long-distance vertical and horizontal scanning. Moreover, these LIDARs easily provide long-distance operation exceeding 30–50 km [9, 10] and 25 km by our LIDAR system [11]. A drawback of this approach arises when one needs a more detailed aerosol fields' characterization in terms of size distribution, nonsphericity or spectral properties of the particles. To satisfy such requirements, various more complex techniques had been developed. For example, UV laserinduced fluorescence allows one to assess the particles' elemental composition based on their electron and rotational-vibrational spectra [15]. The Raman IR spectroscopy technique provides information on the rotational-vibrational spectra of the molecular compounds constituting the particles. The differential absorption LIDAR (DIAL) techniques are also used to determine the presence of gaseous species in the atmosphere based on the absorption of specific laser

The high degree of informativeness of these complex techniques from the viewpoint of a detailed bioaerosol description cannot, unfortunately, be combined with other requirements of importance for their efficient inclusion in networks for air-quality monitoring of the low atmosphere in urban areas. Here we will point out just the more important constraints: (i) high technical complexity, size, and cost of the LIDAR systems; (ii) short operative distances (2–5 km), seriously limiting the possibility of implementing bioaerosol monitoring system in large cities; and (iii) using the laser radiation focusing on the

The purpose of our investigations described below was to explore experimentally the possibility to make use of Mie scattering in a relatively simple LIDAR configuration emitting horizontally the near-ground atmosphere, thereby sounding from a single point the territory of a large city with high angular and distance resolutions. We present successively the LIDAR and sampling equipment in their concurrent functioning, as well as the results of the structural and biological studies, thus proving the capabilities of the LIDAR biomonitoring technique functioning within an urban air-quality monitoring

The methodology for performing the combined LIDAR bio-measurements, capable to be applied in near real-time regimes, is illustrated in **Figure 1**. In general its operation is based on the use of two independent subsystems: (i) LIDAR longdistance sensing subsystem, containing scanning LIDAR for fast 3D mapping aerosol fields (distance, time, mass concentration), and (ii) aerosol sampling equipment of the aerosol particles, transporting bio-contaminations disposed near the LIDAR illuminated resolution volume. The sampled bioaerosol particles are then processed by different methods and algorithms for determination of the mass concentration and LIDAR calibration, bio-contaminant characterization by different techniques, etc. The fast processing of calibrated LIDAR data provides opportunities for

**1.2 Methodology of combined LIDAR bioaerosol measurements**

*DOI: http://dx.doi.org/10.5772/intechopen.87031*

ricin, etc.

light wavelengths [16].

aerosol particles, etc.

and information system.

### *Long-Distance LIDAR Mapping Schematic for Fast Monitoring of Bioaerosol Pollution over Large… DOI: http://dx.doi.org/10.5772/intechopen.87031*

and Raman [9, 10] scattering and laser-induced fluorescence of bioaerosols [14]. Depolarization LIDAR techniques are also employed, whereby measuring the laser light depolarization, and based on models of non-spherical aerosols, the latter's origin can be distinguished, e.g., dust, smoke, pollen, including bioagents as anthrax, ricin, etc.

The Mie scattering-based LIDAR techniques of bioaerosol remote sounding allow one to use simple and inexpensive LIDAR equipment efficient in long-distance vertical and horizontal scanning. Moreover, these LIDARs easily provide long-distance operation exceeding 30–50 km [9, 10] and 25 km by our LIDAR system [11]. A drawback of this approach arises when one needs a more detailed aerosol fields' characterization in terms of size distribution, nonsphericity or spectral properties of the particles. To satisfy such requirements, various more complex techniques had been developed. For example, UV laserinduced fluorescence allows one to assess the particles' elemental composition based on their electron and rotational-vibrational spectra [15]. The Raman IR spectroscopy technique provides information on the rotational-vibrational spectra of the molecular compounds constituting the particles. The differential absorption LIDAR (DIAL) techniques are also used to determine the presence of gaseous species in the atmosphere based on the absorption of specific laser light wavelengths [16].

The high degree of informativeness of these complex techniques from the viewpoint of a detailed bioaerosol description cannot, unfortunately, be combined with other requirements of importance for their efficient inclusion in networks for air-quality monitoring of the low atmosphere in urban areas. Here we will point out just the more important constraints: (i) high technical complexity, size, and cost of the LIDAR systems; (ii) short operative distances (2–5 km), seriously limiting the possibility of implementing bioaerosol monitoring system in large cities; and (iii) using the laser radiation focusing on the aerosol particles, etc.

The purpose of our investigations described below was to explore experimentally the possibility to make use of Mie scattering in a relatively simple LIDAR configuration emitting horizontally the near-ground atmosphere, thereby sounding from a single point the territory of a large city with high angular and distance resolutions. We present successively the LIDAR and sampling equipment in their concurrent functioning, as well as the results of the structural and biological studies, thus proving the capabilities of the LIDAR biomonitoring technique functioning within an urban air-quality monitoring and information system.

#### **1.2 Methodology of combined LIDAR bioaerosol measurements**

The methodology for performing the combined LIDAR bio-measurements, capable to be applied in near real-time regimes, is illustrated in **Figure 1**. In general its operation is based on the use of two independent subsystems: (i) LIDAR longdistance sensing subsystem, containing scanning LIDAR for fast 3D mapping aerosol fields (distance, time, mass concentration), and (ii) aerosol sampling equipment of the aerosol particles, transporting bio-contaminations disposed near the LIDAR illuminated resolution volume. The sampled bioaerosol particles are then processed by different methods and algorithms for determination of the mass concentration and LIDAR calibration, bio-contaminant characterization by different techniques, etc. The fast processing of calibrated LIDAR data provides opportunities for

*Atmospheric Air Pollution and Monitoring*

atmospheric aerosols [1]. However, surprisingly little is known regarding the genesis, composition, or dynamics of the atmosphere's microbial inhabitants, particle's chemical composition, particle's surface pollution, and their relation with the PM [2, 3]. In what concerns human health, the most harmful ones are the particles with sizes below 10 μm, standardized in terms of permissible concentration as PM2.5 and PM10. The fast pace of the information technology development in the first decade of twenty-first century created extraordinary possibilities for organizing systems for real-time monitoring of the atmosphere over urban areas. The implementation of such techniques requires the development of modern fast sensor systems, whose principle of operation, size, and convenient management ensures a problem-free functioning of the information systems without affecting the life in the particular urban area. The well-known techniques of PM monitoring include as a rule a small number of stations and the use of specialized algorithms for rapid assessment of the aerosol pollution's spatial structure above the city. These approaches suffer from a number of drawbacks, such as limited spatial resolution as compared with the urban structure, the low temporal resolution from the viewpoint of the quick polluting processes, and following the PM proliferation, the use of non-calibrated devices, and the subjectivity in selecting the sampling sites. A major drawback of the techniques in question is the relatively slow determination of the pollution sources. Of particular interest in assessing the air quality in urban areas is the biological contamination transported by PM in the form of bioaerosols. According to [4], almost 25% of the total airborne PM above land surfaces contains biological materials in the form of pollens, fungal spores, bacteria, viruses, and fragments from plants, animals, or living organisms with a size ranging from 0.02 up to

The present chapter discusses the capabilities of remote techniques of analyzing the pollution fields in conjunction with in situ sampling of bioaerosols and investigation of PM pollution as one of the promising approaches to raising the efficiency of systems of air monitoring over urban areas [9–11]. The light detection and ranging (LIDAR) techniques are considered as being among the ones best suited to real-time scanning and/or long-term monitoring of the pollution above large areas. Their advantages arise from several factors: (1) light wavelengths commensurate with or close to the PM size, (2) good resolution in terms of distance (5–30 m) and

mode of operation, the LIDAR techniques allow one to construct LIDAR maps of the aerosol pollution distribution over large cities, which can be easily juxtaposed with the ground atmospheric monitoring networks. Special attention was paid here to the development of a specific LIDAR measurement schematic to be suitably combined with in situ bioaerosol sampling investigating its crystallo-chemical and surface structure, including the presence of bacteria and adsorbed nano- and micron-sized organic and inorganic contamination. The successful applicability of such combined methodology is demonstrated below by the results of a study of the РМ size distribution and genesis in two typical urban zones within a large city (Sofia, capital of Bulgaria, population of approx. 1.3 million), in places where the LIDAR monitoring had established previously a mass concentration in peak hours

In the literature [13] one can find descriptions of various LIDAR techniques based on the interaction of laser radiation with bioaerosols, such as Mie, Rayleigh,

), and (3) range of operation (20–30 km) [12]. In a scanning

**50**

100 μm [5–8].

elevation angle (~1o

of vehicle traffic.

**1.1 LIDAR technologies applied in aerosol sensing**

**Figure 1.** *Schematic diagram of a LIDAR bioaerosol measurement system.*

well-timed decision-making for prevention of further spreading of bio-pollutants over the entire city area.

The LIDAR equipment is disposed in a single point (1). The laser beam is directed along specific paths (2) partly overlapping major city thoroughfares (3) with heavy traffic. These directions are selected on the basis of preliminary estimates (by, e.g., city monitoring network) of the presence of localized sources (4) emitting biocontaminants that are subsequently transported in the near-ground atmosphere by PM. The PM sampling equipment (5) is placed close to the heavy-traffic spots, as explained below. In this schematic the scanning LIDAR capabilities could provide an effective coverage of too large areas of radius up to 25 km (1000 km2 and more).

#### *1.2.1 LIDAR sensing subsystem*

LIDAR sensing subsystem for mapping the aerosol field above the sampling device (5) (**Figure 1**). An important point here is the contribution of the automobile traffic creating sufficiently strong backscattered LIDAR signals due to the PM, emitted by internal combustion engines. As these are formed at high temperatures, the possibility of its initially containing microorganisms is negligible. The hot PMs are then lifted up quickly in the air and enter the field of proliferation of bio-contaminants originating from the sources (4) (**Figure 1**). Thus, PM serves as a transport medium for the bioaerosols spreading over the city. In hours of heavy traffic, the backscattered LIDAR signal from bioaerosol-bearing PM is strong enough to allow remote sounding to distances of up to 10 km and more. After a fast computer processing, the LIDAR maps constructed can directly be used by the air-quality monitoring systems.

#### *1.2.2 Sampling subsystem*

It is described in detail below when we analyze the bio-contaminants found in the LIDAR sounding zones. The sampling is independent of the LIDAR subsystem, but it is important to synchronize the sampling time and duration with the LIDAR sounding in the vicinity of the sampling site. The LIDAR map can be used (with the aid of specialized algorithms) to establish the probable locations

**53**

**Figure 2.**

*Long-Distance LIDAR Mapping Schematic for Fast Monitoring of Bioaerosol Pollution over Large…*

The LIDAR subsystem used in the experiments described here is of the scanning type with capability of scanning the entire hemisphere with an angular resolution

 and is part of the EARLINET and ACTRIS LIDAR Station of Institute of Electronics—Вulgarian Academy of Sciences (IE-BAS) (see **Figure 2a**). It has been, and is, used in a large number of experiments of observation, monitoring, and mapping of the transport (including the transborder) of aerosol loadings over the city of Sofia [15]. The emitter is a CuBr vapor laser, with an output power of 2–4 W, pulse repetition rate of 5–10 kHz, and pulse duration of ~10 ns, the probing wavelength emitted being 510.6 nm. The receiving part operates in a photon-counting mode allowing sounding at distances up to *R*max ~ 25–30 km [11]. The diameter of the Cassegrain-type receiving telescope is 19 cm. The LIDAR profiles are recorded and processed by a computer by the known algorithms [17, 18] and are then presented as multidimensional (depending on the sounding geometry) LIDAR maps. The laser beam divergence after collimation is within 1–2 mrad and can be varied if necessary. The receiving angle of the optical system can also be varied by varying the diameter of the diaphragm in front of the photon counter. Further, the spatial resolution is chosen within the 15–30 m range (usually 30 m); thus, the scattering volume is about twice as large as the volume pumped during the time of measure-

**Figure 2b** presents a vertical cross section of the atmosphere with origin at the LIDAR station and directed to downtown Sofia along the city's main thoroughfare, Tsarigradsko Chaussee Blvd. One clearly sees the vertical structure of the atmospheric aerosol density (in terms of backscatter coefficient), comprising a well-expressed ground layer with a height of 500–600 m, together with other

*(а) Cu vapor LIDAR for atmospheric scanning; (b) LIDAR map of a vertical cross section of the atmosphere with origin at the IE-BAS LIDAR Station; (c) LIDAR map of the near-ground atmosphere of a city zone with* 

*high and low degree of PM pollution color-coded by brown and blue, respectively.*

of sources of PM and bio- and other pollutants. The sampling data allow us to calibrate the LIDAR signal in terms of mass concentration (see below), thus shortening the time of reaction to the appearance of PM and other pollutants based on LIDAR observations only. We should note here that the delay due to processing the results as compared with the time necessary to process the LIDAR data (nearly real time) will not cause problems in the functioning of the entire system together with the city monitoring system. The sampling data will be saved and documented

*DOI: http://dx.doi.org/10.5772/intechopen.87031*

and could thus be used at a later stage.

ment by the aerosol sampler located nearby.

**2. Techniques and equipment**

**2.1 LIDAR subsystem**

of ~1o

*Long-Distance LIDAR Mapping Schematic for Fast Monitoring of Bioaerosol Pollution over Large… DOI: http://dx.doi.org/10.5772/intechopen.87031*

of sources of PM and bio- and other pollutants. The sampling data allow us to calibrate the LIDAR signal in terms of mass concentration (see below), thus shortening the time of reaction to the appearance of PM and other pollutants based on LIDAR observations only. We should note here that the delay due to processing the results as compared with the time necessary to process the LIDAR data (nearly real time) will not cause problems in the functioning of the entire system together with the city monitoring system. The sampling data will be saved and documented and could thus be used at a later stage.

## **2. Techniques and equipment**

#### **2.1 LIDAR subsystem**

*Atmospheric Air Pollution and Monitoring*

over the entire city area.

*Schematic diagram of a LIDAR bioaerosol measurement system.*

**Figure 1.**

*1.2.1 LIDAR sensing subsystem*

air-quality monitoring systems.

*1.2.2 Sampling subsystem*

well-timed decision-making for prevention of further spreading of bio-pollutants

LIDAR sensing subsystem for mapping the aerosol field above the sampling device (5) (**Figure 1**). An important point here is the contribution of the automobile traffic creating sufficiently strong backscattered LIDAR signals due to the PM, emitted by internal combustion engines. As these are formed at high temperatures, the possibility of its initially containing microorganisms is negligible. The hot PMs are then lifted up quickly in the air and enter the field of proliferation of bio-contaminants originating from the sources (4) (**Figure 1**). Thus, PM serves as a transport medium for the bioaerosols spreading over the city. In hours of heavy traffic, the backscattered LIDAR signal from bioaerosol-bearing PM is strong enough to allow remote sounding to distances of up to 10 km and more. After a fast computer processing, the LIDAR maps constructed can directly be used by the

It is described in detail below when we analyze the bio-contaminants found in the LIDAR sounding zones. The sampling is independent of the LIDAR subsystem, but it is important to synchronize the sampling time and duration with the LIDAR sounding in the vicinity of the sampling site. The LIDAR map can be used (with the aid of specialized algorithms) to establish the probable locations

effective coverage of too large areas of radius up to 25 km (1000 km2

The LIDAR equipment is disposed in a single point (1). The laser beam is directed along specific paths (2) partly overlapping major city thoroughfares (3) with heavy traffic. These directions are selected on the basis of preliminary estimates (by, e.g., city monitoring network) of the presence of localized sources (4) emitting biocontaminants that are subsequently transported in the near-ground atmosphere by PM. The PM sampling equipment (5) is placed close to the heavy-traffic spots, as explained below. In this schematic the scanning LIDAR capabilities could provide an

and more).

**52**

The LIDAR subsystem used in the experiments described here is of the scanning type with capability of scanning the entire hemisphere with an angular resolution of ~1o and is part of the EARLINET and ACTRIS LIDAR Station of Institute of Electronics—Вulgarian Academy of Sciences (IE-BAS) (see **Figure 2a**). It has been, and is, used in a large number of experiments of observation, monitoring, and mapping of the transport (including the transborder) of aerosol loadings over the city of Sofia [15]. The emitter is a CuBr vapor laser, with an output power of 2–4 W, pulse repetition rate of 5–10 kHz, and pulse duration of ~10 ns, the probing wavelength emitted being 510.6 nm. The receiving part operates in a photon-counting mode allowing sounding at distances up to *R*max ~ 25–30 km [11]. The diameter of the Cassegrain-type receiving telescope is 19 cm. The LIDAR profiles are recorded and processed by a computer by the known algorithms [17, 18] and are then presented as multidimensional (depending on the sounding geometry) LIDAR maps. The laser beam divergence after collimation is within 1–2 mrad and can be varied if necessary. The receiving angle of the optical system can also be varied by varying the diameter of the diaphragm in front of the photon counter. Further, the spatial resolution is chosen within the 15–30 m range (usually 30 m); thus, the scattering volume is about twice as large as the volume pumped during the time of measurement by the aerosol sampler located nearby.

**Figure 2b** presents a vertical cross section of the atmosphere with origin at the LIDAR station and directed to downtown Sofia along the city's main thoroughfare, Tsarigradsko Chaussee Blvd. One clearly sees the vertical structure of the atmospheric aerosol density (in terms of backscatter coefficient), comprising a well-expressed ground layer with a height of 500–600 m, together with other

#### **Figure 2.**

*(а) Cu vapor LIDAR for atmospheric scanning; (b) LIDAR map of a vertical cross section of the atmosphere with origin at the IE-BAS LIDAR Station; (c) LIDAR map of the near-ground atmosphere of a city zone with high and low degree of PM pollution color-coded by brown and blue, respectively.*

aerosol formations of limited size. The aerosol loading of the near-ground layer over the urbanized part of Sofia City Municipality is due to various sources (both local and transborder), with the main one being heavy automobile traffic, which is the subject of the present discussion. **Figure 2c** is a LIDAR map of part of the urban area, where one can see again well-defined zones of high (brown) and low (blue) MP pollution.

#### **2.2 Calibration of LIDAR extinction and backscattering coefficients**

Analyzing the PM aerosol loadings formed in the vicinity of heavy-traffic urban areas and experimentally measured by the LIDAR technique schematically presented in **Figure 2**, we were able to draw the important conclusion that the mass concentration of the aerosol loading of hot PM emitted by internal combustion engines is a key parameter when one applies a LIDAR-based methodology to air-quality monitoring in a large city. We thus calibrated the two major LIDAR parameters, namely, the extinction coefficient *α(r)* and the backscatter coefficient *β*(*r*), in terms of the aerosol mass concentration following the well-known method [17, 19] and making use of the mass concentration *Ma* data obtained by the sampling device. For the LIDAR ratio *LiR* = *α*(*r*)/*β*(*r*), we adopted the typical value of *LiR* = 50 [16, 19]. The parameters *β*(*r*) and *α*(*r*) were calculated using the LIDAR equation under the assumption of a horizontally homogeneous atmosphere:

$$P(r) = P\_0 \frac{c\tau}{2} C \frac{\beta(r)}{r^2} \exp\left[-2\int\_{r\_0}^{r} a(r) dr\right] \tag{1}$$

where *P*(*r*) is the power of the detected laser radiation backscattered from the atmosphere from a distance *r* = \_\_*ct* <sup>2</sup> after a period of time *t* following the moment of laser pulse emission and *τ* is the pulse duration. Under the homogeneity assumption, the extinction coefficient α(*r*) is calculated as.

$$\mathfrak{a}(r) = -\frac{1}{2}\frac{dS(r)}{dr}, \text{where } S(r) = \ln\left[r^2 P(r)\right] \tag{2}$$

**55**

*Long-Distance LIDAR Mapping Schematic for Fast Monitoring of Bioaerosol Pollution over Large…*

The average intensity of the traffic according to Sofia Municipality is about 6000 vehicles per hour (Sofia Municipality Report 01.10.2017). The second sampling point was located at (ii) green area (GA) which was chosen at about 6.5 km (600 m ASL) from the LIDAR station on the roof of the Faculty of Biology of St. Kl. Ohridski University of Sofia; the building is located at a relatively busy thoroughfare, which forms a borderline between a green residential area and the largest city park.

*Calibration plots for direct calculation of the aerosol mass concentration by both the extinction (a) and* 

Our LIDAR observation schedule complied with the generally accepted manner of measuring the aerosol mass concentration by air-quality monitoring systems. The sampling device pumps atmospheric air through the filter (typically a volume of 60–100 m3

for an interval of about a few hours. Thus, the laser beam was stationary and directed to pass above the aspirator at a height of *hPM* < *R*, *R*~30 m being the LIDAR's radial resolution (point 1, **Figure 1**). The height of placing the aspirator was also chosen to comply with this condition, *hasp* < *R*. We thus could assume that we could neglect the vertical inhomogeneities of the atmospheric density. The LIDAR signals represent the number of backscattered photons *Lphot*(*k*.*R*,τ*m*), where *k* = 1..*K* max, *K*max = *R*max/*R*, and τ*<sup>m</sup>* = 5 min are the time interval of photon accumulation. The total time of measurement lasted from 1 to several hours, depending on the particular weather situation. The computer system processes the input data by solving the LIDAR Eq. (1), with its output being profiles of the backscatter coefficient β(*k*.*R*) or the extinction coefficient ε(*k*.*R*), as calibrated in terms of aerosol mass concentration (see **Figure 3**). The set of LIDAR profiles obtained for the entire period of measurement is used to construct 3D LIDAR maps, with the *x* axis presenting the accumulation time with a step of τ*<sup>m</sup>* = 5 min and the *y* axis the distance from the LIDAR with a step *R*. The *z* axis corresponds to the color-coded coefficients of backscatter or extinction. Thus, the position of the LIDAR

station on the map has coordinates (0,0) at the start of measurements.

of τ*<sup>m</sup>* = 5 min). The duration of sounding τ*prob* = *J*max.τ*m* exceeded 3 h.

The series of figures below presents a set of such 3D LIDAR maps illustrating the aerosol loading space-time distribution within the region of LIDAR sounding and the aerosol sampler disposition. **Figure 4a** and **b** is a 3D LIDAR map of the backscatter coefficient distribution β(*k*.*R*,*j*.τ *<sup>m</sup>*), *k* = 1..*K* max, and *j* = 1..*J* max; the vertical axis presents the distance to the LIDAR station with a step of *R* = 30 m and *K*max.*R* > 10 km, and the horizontal axis is the time (UTC) elapsed since the LIDAR sounding start (step of τ*<sup>m</sup>* = 5 min). **Figure 4a** and **b** is 3D LIDAR maps of the backscatter coefficient distribution β(*k*.*R*,*j*.τ *<sup>m</sup>*), *k* = 1..*K* max, and *j* = 1..*J* max; the vertical axis presents the distance to the LIDAR station with a step of *R* = 30 m and *K*max.*R* > 10 km, and the horizontal axis is the time (UTC) elapsed since the LIDAR sounding starts (step

)

*DOI: http://dx.doi.org/10.5772/intechopen.87031*

**Figure 3.**

*backscattering (b) coefficients.*

**Figure 3a** and **b** presents the calibration dependencies of the mass concentration in [mg/m3 ] of, respectively, α(*r*) and β(*r*). In both cases, the linear fit (Y = A + B.x) shows acceptable values of the standard deviation (less than 4%) and the correlation coefficient (over 0.92). The plots in **Figure 3** can be used directly for calibrating the LIDAR maps, shown above, in mass concentration.

#### **2.3 LIDAR images of PM fields along the sounding directions**

The city of Sofia is located in a valley surrounded by several mountains, which determine the meteorological conditions characterized by reduced possibility of self-cleaning of the atmosphere. Near-ground temperature inversions occur very often in the winter-spring transition period, on windless days, and in a stable atmosphere, with negative ecological effects due to the retention layer formed leading to increased concentration of pollutants in the boundary ground layer. The weather particularities motivated our choice of period of experimental observations, namely, February-March 2018 and 2019.

The object of our studies was the air pollution on windless days at two typical urban area points. (i) Intensive traffic (IT)—Tsarigradsko Chaussee Blvd. is the busiest thoroughfare in Sofia city with grade-separated dual carriageway in almost its entire length of 11.4 km, running from the northwest to the southeast. *Long-Distance LIDAR Mapping Schematic for Fast Monitoring of Bioaerosol Pollution over Large… DOI: http://dx.doi.org/10.5772/intechopen.87031*

**Figure 3.**

*Atmospheric Air Pollution and Monitoring*

MP pollution.

aerosol formations of limited size. The aerosol loading of the near-ground layer over the urbanized part of Sofia City Municipality is due to various sources (both local and transborder), with the main one being heavy automobile traffic, which is the subject of the present discussion. **Figure 2c** is a LIDAR map of part of the urban area, where one can see again well-defined zones of high (brown) and low (blue)

Analyzing the PM aerosol loadings formed in the vicinity of heavy-traffic urban areas and experimentally measured by the LIDAR technique schematically presented in **Figure 2**, we were able to draw the important conclusion that the mass concentration of the aerosol loading of hot PM emitted by internal combustion engines is a key parameter when one applies a LIDAR-based methodology to air-quality monitoring in a large city. We thus calibrated the two major LIDAR parameters, namely, the extinction coefficient *α(r)* and the backscatter coefficient *β*(*r*), in terms of the aerosol mass concentration following the well-known method [17, 19] and making use of the mass concentration *Ma* data obtained by the sampling device. For the LIDAR ratio *LiR* = *α*(*r*)/*β*(*r*), we adopted the typical value of *LiR* = 50 [16, 19]. The parameters *β*(*r*) and *α*(*r*) were calculated using the LIDAR equation

**2.2 Calibration of LIDAR extinction and backscattering coefficients**

under the assumption of a horizontally homogeneous atmosphere:

<sup>2</sup> *<sup>C</sup>β*(*r*) \_\_\_\_

*<sup>r</sup>*<sup>2</sup> *exp*[−2 <sup>∫</sup>*<sup>r</sup>*<sup>0</sup>

where *P*(*r*) is the power of the detected laser radiation backscattered from the

**Figure 3a** and **b** presents the calibration dependencies of the mass concentration

shows acceptable values of the standard deviation (less than 4%) and the correlation coefficient (over 0.92). The plots in **Figure 3** can be used directly for calibrat-

The city of Sofia is located in a valley surrounded by several mountains, which determine the meteorological conditions characterized by reduced possibility of self-cleaning of the atmosphere. Near-ground temperature inversions occur very often in the winter-spring transition period, on windless days, and in a stable atmosphere, with negative ecological effects due to the retention layer formed leading to increased concentration of pollutants in the boundary ground layer. The weather particularities motivated our choice of period of experimental observa-

The object of our studies was the air pollution on windless days at two typical urban area points. (i) Intensive traffic (IT)—Tsarigradsko Chaussee Blvd. is the busiest thoroughfare in Sofia city with grade-separated dual carriageway in almost its entire length of 11.4 km, running from the northwest to the southeast.

] of, respectively, α(*r*) and β(*r*). In both cases, the linear fit (Y = A + B.x)

laser pulse emission and *τ* is the pulse duration. Under the homogeneity assump-

*<sup>r</sup> α*(*r*)*dr*] (1)

<sup>2</sup> after a period of time *t* following the moment of

*dr* ,where *<sup>S</sup>*(*r*) <sup>=</sup> ln[*r*<sup>2</sup>*P*(*r*)] (2)

*P*(*r*) = *P*<sup>0</sup> \_\_*c*

tion, the extinction coefficient α(*r*) is calculated as.

2 \_\_\_\_\_ *dS*(*r*)

ing the LIDAR maps, shown above, in mass concentration.

tions, namely, February-March 2018 and 2019.

**2.3 LIDAR images of PM fields along the sounding directions**

atmosphere from a distance *r* = \_\_*ct*

α(*r*) = −\_1

in [mg/m3

**54**

*Calibration plots for direct calculation of the aerosol mass concentration by both the extinction (a) and backscattering (b) coefficients.*

The average intensity of the traffic according to Sofia Municipality is about 6000 vehicles per hour (Sofia Municipality Report 01.10.2017). The second sampling point was located at (ii) green area (GA) which was chosen at about 6.5 km (600 m ASL) from the LIDAR station on the roof of the Faculty of Biology of St. Kl. Ohridski University of Sofia; the building is located at a relatively busy thoroughfare, which forms a borderline between a green residential area and the largest city park.

Our LIDAR observation schedule complied with the generally accepted manner of measuring the aerosol mass concentration by air-quality monitoring systems. The sampling device pumps atmospheric air through the filter (typically a volume of 60–100 m3 ) for an interval of about a few hours. Thus, the laser beam was stationary and directed to pass above the aspirator at a height of *hPM* < *R*, *R*~30 m being the LIDAR's radial resolution (point 1, **Figure 1**). The height of placing the aspirator was also chosen to comply with this condition, *hasp* < *R*. We thus could assume that we could neglect the vertical inhomogeneities of the atmospheric density. The LIDAR signals represent the number of backscattered photons *Lphot*(*k*.*R*,τ*m*), where *k* = 1..*K* max, *K*max = *R*max/*R*, and τ*<sup>m</sup>* = 5 min are the time interval of photon accumulation. The total time of measurement lasted from 1 to several hours, depending on the particular weather situation. The computer system processes the input data by solving the LIDAR Eq. (1), with its output being profiles of the backscatter coefficient β(*k*.*R*) or the extinction coefficient ε(*k*.*R*), as calibrated in terms of aerosol mass concentration (see **Figure 3**). The set of LIDAR profiles obtained for the entire period of measurement is used to construct 3D LIDAR maps, with the *x* axis presenting the accumulation time with a step of τ*<sup>m</sup>* = 5 min and the *y* axis the distance from the LIDAR with a step *R*. The *z* axis corresponds to the color-coded coefficients of backscatter or extinction. Thus, the position of the LIDAR station on the map has coordinates (0,0) at the start of measurements.

The series of figures below presents a set of such 3D LIDAR maps illustrating the aerosol loading space-time distribution within the region of LIDAR sounding and the aerosol sampler disposition. **Figure 4a** and **b** is a 3D LIDAR map of the backscatter coefficient distribution β(*k*.*R*,*j*.τ *<sup>m</sup>*), *k* = 1..*K* max, and *j* = 1..*J* max; the vertical axis presents the distance to the LIDAR station with a step of *R* = 30 m and *K*max.*R* > 10 km, and the horizontal axis is the time (UTC) elapsed since the LIDAR sounding start (step of τ*<sup>m</sup>* = 5 min). **Figure 4a** and **b** is 3D LIDAR maps of the backscatter coefficient distribution β(*k*.*R*,*j*.τ *<sup>m</sup>*), *k* = 1..*K* max, and *j* = 1..*J* max; the vertical axis presents the distance to the LIDAR station with a step of *R* = 30 m and *K*max.*R* > 10 km, and the horizontal axis is the time (UTC) elapsed since the LIDAR sounding starts (step of τ*<sup>m</sup>* = 5 min). The duration of sounding τ*prob* = *J*max.τ*m* exceeded 3 h.

#### **Figure 4.**

*(a and b) Hourly aerosol pollution loading (as 3D LIDAR maps) over the urban area with intensive traffic. LIDAR image (c) is overlapped in vertical position on the Google Maps (d) of the Sofia central part. Position of the LIDAR station is well seen. Such presentation is useful for simultaneous space-time tracing of the strong PM formation over the monitored area. (e) and (f) 3D LIDAR maps, demonstrating cases of complicated space-time dynamics of aerosol fields, transporting bioaerosol, emitted mainly by different local regions of the large city.*

The backscatter coefficient β(*k*.*R*,*j*.τ *<sup>m</sup>*) is color-coded in terms of mass concentration, with the dark brown areas corresponding to the zones of the highest PM concentration and the light blue ones, to the lowest PM concentration. As seen, this approach allowed to follow the temporal behavior of the PM aerosol loading along the LIDAR sounding path to a distance of more than 10 km. For example, **Figure 4a** demonstrates that in the early afternoon hours, where the automobile traffic is relatively light, the aerosol loading is low, especially in the zones away from the downtown area. As

**57**

~250–300 m3

).

*Long-Distance LIDAR Mapping Schematic for Fast Monitoring of Bioaerosol Pollution over Large…*

the working day is nearing its end, the traffic intensity rises, and, correspondingly, the LIDAR response shows increasing mass concentration along the boulevard. The distances near the LIDAR station are characterized by very high mass concentration, since the LIDAR station (IE-BAS) is located close to the busy road junction—Tsarigradsko Chaussee Blvd. crossing with another four-lane boulevard. The image in **Figure 4b** illustrates the passing of a larger aerosol formation of length to the order of 8 km along the boulevard. In such cases we typically founded heavy bioaerosol loadings, emitted

The images in **Figure 4c** illustrate a relatively small aerosol formation (as in **Figure 4b**), passing near the LIDAR station. The same image can be overlapped (in vertical disposition) on the Google Maps of the Sofia central part (**Figure 4c**) for better identification of the disposition of the emitted aerosol formation. We should emphasize here the important point that the LIDAR maps can be used to follow the bioaerosol transport and to estimate the probable location of their sources. One could also draw the conclusion that, when the methodology discussed is employed, the sampling device location is not so critical in what concerns observing microorganisms in the air and establishing the contamination sources. The incorporation of the proposed LIDAR-based methodology into the city monitoring system could provide many additional advantages, such as forecasting the effects of selected bioaerosol loadings on the entire large city area, e.g., using modeling algorithms. The successful scanning by the LIDAR system in different directions (see **Figure 1**) could provide additional information for improving the local authorities' decision-making process.

In the experiments presented, we directed the LIDAR beam to intense traffic area and green area to probe the near-surface atmosphere in a constant horizontal direction. The measurement time covered practically the entire period of the late afternoon traffic maximums, while maximums of the PM pollutions were clearly observed in the backscattered LIDAR signals, as received and processed by our system. The samples were taken in situ using a Hygitest 106 (Maimex), a highefficiency portable device for sampling and concentration determination of PM in atmospheric aerosol. The apparatus allows simultaneously to take samples at four canals with a possibility to regulate the volume of air passing through the filters. The flow-rate of the aspirated air was measured by the sampling unit. The sampled volume was chosen to be smaller than the LIDAR resolution volume (typically

The dust was collected on a filter (borosilicon oxide) with #3 μm and #8 μm (FILTER-LAB, Material MCE, Lot.180509006 and 07). Also analytical filters #0.2 μm type FPP-15-2568-411-05795731-2008 were used, consisting of a layer of ultrathin threads (diameter of 1.5 μm) deposited on a piece of fabric and designed to collect aerosol particles of size exceeding 0.2 μm. Additionally, the material collected in situ on the filters after 3 h of aspiration during the LIDAR monitoring was studied by a number of methods: metal analysis (MA) has been carried out by devices and equipment—ICP-OES, PlasmaQuant PQ 9000 Elite (Analytik Jena), sample visualization, and PM morphological by scanning electron microscopy (SEM) and energy-dispersive and X-ray fluorescence systems (EDAX). Chemical composition, phase analysis, and particle size distribution were made using powder X-ray diffraction (XRD), photoelectron spectroscopy (XPS), and infrared (IR) spectroscopy. Phase identification was performed with the X-Pert program using ICDD-PDF2. Important tools for structural measurement was Mössbauer analysis which was made using apparatus Wissenschaftliche Elektronik GmbH, working with a constant acceleration mode: 57Co/Cr source, α-Fe standard. The sampling

.

by the near disposed populated regions of limited areas of order of 1–2 km2

*DOI: http://dx.doi.org/10.5772/intechopen.87031*

**2.4 Sampling and analytical methods**

#### *Long-Distance LIDAR Mapping Schematic for Fast Monitoring of Bioaerosol Pollution over Large… DOI: http://dx.doi.org/10.5772/intechopen.87031*

the working day is nearing its end, the traffic intensity rises, and, correspondingly, the LIDAR response shows increasing mass concentration along the boulevard. The distances near the LIDAR station are characterized by very high mass concentration, since the LIDAR station (IE-BAS) is located close to the busy road junction—Tsarigradsko Chaussee Blvd. crossing with another four-lane boulevard. The image in **Figure 4b** illustrates the passing of a larger aerosol formation of length to the order of 8 km along the boulevard. In such cases we typically founded heavy bioaerosol loadings, emitted by the near disposed populated regions of limited areas of order of 1–2 km2 .

The images in **Figure 4c** illustrate a relatively small aerosol formation (as in **Figure 4b**), passing near the LIDAR station. The same image can be overlapped (in vertical disposition) on the Google Maps of the Sofia central part (**Figure 4c**) for better identification of the disposition of the emitted aerosol formation.

We should emphasize here the important point that the LIDAR maps can be used to follow the bioaerosol transport and to estimate the probable location of their sources. One could also draw the conclusion that, when the methodology discussed is employed, the sampling device location is not so critical in what concerns observing microorganisms in the air and establishing the contamination sources. The incorporation of the proposed LIDAR-based methodology into the city monitoring system could provide many additional advantages, such as forecasting the effects of selected bioaerosol loadings on the entire large city area, e.g., using modeling algorithms. The successful scanning by the LIDAR system in different directions (see **Figure 1**) could provide additional information for improving the local authorities' decision-making process.

### **2.4 Sampling and analytical methods**

In the experiments presented, we directed the LIDAR beam to intense traffic area and green area to probe the near-surface atmosphere in a constant horizontal direction. The measurement time covered practically the entire period of the late afternoon traffic maximums, while maximums of the PM pollutions were clearly observed in the backscattered LIDAR signals, as received and processed by our system. The samples were taken in situ using a Hygitest 106 (Maimex), a highefficiency portable device for sampling and concentration determination of PM in atmospheric aerosol. The apparatus allows simultaneously to take samples at four canals with a possibility to regulate the volume of air passing through the filters. The flow-rate of the aspirated air was measured by the sampling unit. The sampled volume was chosen to be smaller than the LIDAR resolution volume (typically ~250–300 m3 ).

The dust was collected on a filter (borosilicon oxide) with #3 μm and #8 μm (FILTER-LAB, Material MCE, Lot.180509006 and 07). Also analytical filters #0.2 μm type FPP-15-2568-411-05795731-2008 were used, consisting of a layer of ultrathin threads (diameter of 1.5 μm) deposited on a piece of fabric and designed to collect aerosol particles of size exceeding 0.2 μm. Additionally, the material collected in situ on the filters after 3 h of aspiration during the LIDAR monitoring was studied by a number of methods: metal analysis (MA) has been carried out by devices and equipment—ICP-OES, PlasmaQuant PQ 9000 Elite (Analytik Jena), sample visualization, and PM morphological by scanning electron microscopy (SEM) and energy-dispersive and X-ray fluorescence systems (EDAX). Chemical composition, phase analysis, and particle size distribution were made using powder X-ray diffraction (XRD), photoelectron spectroscopy (XPS), and infrared (IR) spectroscopy. Phase identification was performed with the X-Pert program using ICDD-PDF2. Important tools for structural measurement was Mössbauer analysis which was made using apparatus Wissenschaftliche Elektronik GmbH, working with a constant acceleration mode: 57Co/Cr source, α-Fe standard. The sampling

*Atmospheric Air Pollution and Monitoring*

**56**

**Figure 4.**

The backscatter coefficient β(*k*.*R*,*j*.τ *<sup>m</sup>*) is color-coded in terms of mass concentration, with the dark brown areas corresponding to the zones of the highest PM concentration and the light blue ones, to the lowest PM concentration. As seen, this approach allowed to follow the temporal behavior of the PM aerosol loading along the LIDAR sounding path to a distance of more than 10 km. For example, **Figure 4a** demonstrates that in the early afternoon hours, where the automobile traffic is relatively light, the aerosol loading is low, especially in the zones away from the downtown area. As

*(a and b) Hourly aerosol pollution loading (as 3D LIDAR maps) over the urban area with intensive traffic. LIDAR image (c) is overlapped in vertical position on the Google Maps (d) of the Sofia central part. Position of the LIDAR station is well seen. Such presentation is useful for simultaneous space-time tracing of the strong PM formation over the monitored area. (e) and (f) 3D LIDAR maps, demonstrating cases of complicated space-time dynamics of aerosol fields, transporting bioaerosol, emitted mainly by different local regions of the large city.*

for investigation of microbiota in bioaerosols formed was achieved using a Hygitest 106 and aха replica technique on nutrient agar as well as Koch sedimentation method. Different elective media as nutrient agar; nutrient media for oligotrophs, actinomycetes, and fungi; blood agar; phenylethyl alcohol agar; MacConkey agar; and bacteria mobility-test medium [17] were used. Culturable bacteria isolated as pure cultures were identified by the methods of classical taxonomy [18] and the methods of the molecular taxonomy based on the PCR of 16S rDNA with universal eubacterial primers [20]. Molecular identification of the fungal strains was performed by PCR of the rDNA internal transcribed spacers (ITS) and primers ITS1 and ITS4 [21]. Positive PCR products were purified and sequenced (Macrogen Inc. Amsterdam, The Netherlands).

## **3. Particulate matter characterization**

The natural aerosols are mixture of PM with highly varied crystallo-chemical structure bioaerosols, which are mixture from fungal spores, pollen, plant, etc. The particle research evaluation is a need for integrating the development of continuous monitoring technologies for determining both particle mass and chemical, physical, and biological methods for their identification. Such studies are of importance in determining the health and welfare effects of urban pollution and city transportation planning. As illustrated by **Figure 4**, we proved the possibility of a fast detailed remote analysis and monitoring of the air pollution over large urban regions, providing fast estimates of the air pollution transport over the city, as well as determination of pollution source location. Once a place of high PM concentration was localized by the LIDAR, samples were taken as described above. Initially, the particles were immobilized on single or multiple filters. This was followed by covering with a conductive carbon film deposited by sputtering of spectroscopically pure carbon in high vacuum. The experimental procedure allows for observing the particles' morphology and determining their mean size (**Figure 5a** and **b**) by means of SEM, EDAX, and MA.

The SEM images of the material collected on the filters after 3 h of aspiration during the LIDAR monitoring showed a large amount of particles larger than 2.5 μm and some amount of small particles (under 2.5 μm); most of the particles are included in quasi-aggregated structures, where nanosized particles could also be seen. **Figure 5a** illustrates the wide variety of quasi-spherical particles with an

**Figure 5.**

*SEM images of the particles under different magnification; the fibers reveal the filter structure of (a) PM with size* ≥ *3 μm and (b) PM with size between 0.2 and 3 μm.*

**59**

**Figure 6.**

*April 2018 and 2019).*

*Long-Distance LIDAR Mapping Schematic for Fast Monitoring of Bioaerosol Pollution over Large…*

average size of about 2.5 μm. **Figure 5b** shows a typical shape of PM particles with

Diameters of the PМ varied from 20 nm to a more than 10 μm for pollen or plant debris [22–24]. After selecting typical particle images based on 10 points on the filter's surface, the percentage distribution of the particles with different sizes was obtained. The particles' size distribution study was conducted following the technique of random lines crossing particles of various diameters on optical microscopy and SEM images of immobilized PM particles under different magnification. The SEM images of the material collected on the filters after 3 h of aspiration during the LIDAR monitoring showed a large amount of particles. The airborne PM can be divided into three classes (**Figure 6**), fine PM particles 2.5 μm in diameter or smaller, coarse PM particles 2.5–10 μm in diameter, and PM ≥ 10 μm, which differ not only in size but also in source, chemical composition, physical properties, and formation process. Major sources of PM2.5 could be produced by motor vehicles, residential fireplace fossil fuel combustion by industry and wood stoves, vegetation burning, and smelting or other processes [25]. Our investigation for the period of 2 years showed that in the intensive traffic area, the majority of the particles are smaller than 10 μm as produced by automobile exhaust emissions, while in the green area, bigger particles appeared most probably from the wooded zone.

The studies conducted during the winter-spring transition period of 2018 and 2019 highlighted the alarming trend of increased content of the Cu, Fe, and Zn metals over the permissible concentration values [25]. **Figure 7** presents summarized data for two of the locations (blue is limited value, red dot is intensive traffic, and green dot is green area) that were objects of our studies considered here.

The above results were confirmed by EDAX measurements of PM collected by the functional filters. **Figures 8** and **9** demonstrate the presence of Cu and Fe. Less frequently, the presence of Pb was also noticed, again exceeding the permissible

Great attention in the physicochemical characterization of investigated PM samples was paid to compounds related to the observed overconcentrations of certain elements (**Figure 5**). The chemical elements are varying from main component level to trace elements. **Figure 10** shows the representative powder X-ray diffraction

*Particle size distribution in two points in the urban area of Sofia City (for period of investigation February–*

sizes between 2.5 and 10 μm, which are agglomerates of hybrid origin.

**3.1 Particulate matter distribution and metal's concentration analysis**

*DOI: http://dx.doi.org/10.5772/intechopen.87031*

concentration values (**Figure 8**).

**3.2 Crystallo-chemical structure of PM**

*Long-Distance LIDAR Mapping Schematic for Fast Monitoring of Bioaerosol Pollution over Large… DOI: http://dx.doi.org/10.5772/intechopen.87031*

average size of about 2.5 μm. **Figure 5b** shows a typical shape of PM particles with sizes between 2.5 and 10 μm, which are agglomerates of hybrid origin.

### **3.1 Particulate matter distribution and metal's concentration analysis**

Diameters of the PМ varied from 20 nm to a more than 10 μm for pollen or plant debris [22–24]. After selecting typical particle images based on 10 points on the filter's surface, the percentage distribution of the particles with different sizes was obtained. The particles' size distribution study was conducted following the technique of random lines crossing particles of various diameters on optical microscopy and SEM images of immobilized PM particles under different magnification.

The SEM images of the material collected on the filters after 3 h of aspiration during the LIDAR monitoring showed a large amount of particles. The airborne PM can be divided into three classes (**Figure 6**), fine PM particles 2.5 μm in diameter or smaller, coarse PM particles 2.5–10 μm in diameter, and PM ≥ 10 μm, which differ not only in size but also in source, chemical composition, physical properties, and formation process. Major sources of PM2.5 could be produced by motor vehicles, residential fireplace fossil fuel combustion by industry and wood stoves, vegetation burning, and smelting or other processes [25]. Our investigation for the period of 2 years showed that in the intensive traffic area, the majority of the particles are smaller than 10 μm as produced by automobile exhaust emissions, while in the green area, bigger particles appeared most probably from the wooded zone.

The studies conducted during the winter-spring transition period of 2018 and 2019 highlighted the alarming trend of increased content of the Cu, Fe, and Zn metals over the permissible concentration values [25]. **Figure 7** presents summarized data for two of the locations (blue is limited value, red dot is intensive traffic, and green dot is green area) that were objects of our studies considered here.

The above results were confirmed by EDAX measurements of PM collected by the functional filters. **Figures 8** and **9** demonstrate the presence of Cu and Fe. Less frequently, the presence of Pb was also noticed, again exceeding the permissible concentration values (**Figure 8**).

#### **3.2 Crystallo-chemical structure of PM**

Great attention in the physicochemical characterization of investigated PM samples was paid to compounds related to the observed overconcentrations of certain elements (**Figure 5**). The chemical elements are varying from main component level to trace elements. **Figure 10** shows the representative powder X-ray diffraction

**Figure 6.**

*Particle size distribution in two points in the urban area of Sofia City (for period of investigation February– April 2018 and 2019).*

*Atmospheric Air Pollution and Monitoring*

Amsterdam, The Netherlands).

of SEM, EDAX, and MA.

**3. Particulate matter characterization**

for investigation of microbiota in bioaerosols formed was achieved using a Hygitest 106 and aха replica technique on nutrient agar as well as Koch sedimentation method. Different elective media as nutrient agar; nutrient media for oligotrophs, actinomycetes, and fungi; blood agar; phenylethyl alcohol agar; MacConkey agar; and bacteria mobility-test medium [17] were used. Culturable bacteria isolated as pure cultures were identified by the methods of classical taxonomy [18] and the methods of the molecular taxonomy based on the PCR of 16S rDNA with universal eubacterial primers [20]. Molecular identification of the fungal strains was performed by PCR of the rDNA internal transcribed spacers (ITS) and primers ITS1 and ITS4 [21]. Positive PCR products were purified and sequenced (Macrogen Inc.

The natural aerosols are mixture of PM with highly varied crystallo-chemical structure bioaerosols, which are mixture from fungal spores, pollen, plant, etc. The particle research evaluation is a need for integrating the development of continuous monitoring technologies for determining both particle mass and chemical, physical, and biological methods for their identification. Such studies are of importance in determining the health and welfare effects of urban pollution and city transportation planning. As illustrated by **Figure 4**, we proved the possibility of a fast detailed remote analysis and monitoring of the air pollution over large urban regions, providing fast estimates of the air pollution transport over the city, as well as determination of pollution source location. Once a place of high PM concentration was localized by the LIDAR, samples were taken as described above. Initially, the particles were immobilized on single or multiple filters. This was followed by covering with a conductive carbon film deposited by sputtering of spectroscopically pure carbon in high vacuum. The experimental procedure allows for observing the particles' morphology and determining their mean size (**Figure 5a** and **b**) by means

The SEM images of the material collected on the filters after 3 h of aspiration during the LIDAR monitoring showed a large amount of particles larger than 2.5 μm and some amount of small particles (under 2.5 μm); most of the particles are included in quasi-aggregated structures, where nanosized particles could also be seen. **Figure 5a** illustrates the wide variety of quasi-spherical particles with an

*SEM images of the particles under different magnification; the fibers reveal the filter structure of (a) PM with* 

**58**

**Figure 5.**

*size* ≥ *3 μm and (b) PM with size between 0.2 and 3 μm.*

#### **Figure 7.**

*Metals with concentrations exceeding the permissible limiting values (LV); the blue bars indicate the LV (annual average of mg/m3 ), and the red and green bars indicate the average concentrations in an intensive traffic (IT) and a green area (GA), respectively, as estimated by our LIDAR measurements.*

#### **Figure 8.**

*EDS spectrum of element analysis and the insert presents SEM image of the element's distribution on the fixed particle adsorbed on the filters from IT zone (23.03.2019).*

patterns of studied PM samples originated from different areas (IT and GA). The existence of low intensity and broad X-ray diffraction peaks laid on nonselective background in all registered patterns was observed. The main crystallite phases detected in X-ray diffractograms are silicates and aluminosilicates and carbonates. Lesser amounts of different sulfates were found also. Registered X-ray amorphous halos and nonselective background together with small intensity and high width of diffraction peaks of all registered crystallite phases indicate nearly amorphous structure, small particle size, and low crystallinity degree of PM material from both studied locations. The observations are in good consideration with obtained elemental composition and SEM analysis of studied materials. Regarding both analyses it could be concluded that PM greatly vary in size from nanometers to several tenths of micrometers. However, most of the particles on SEM images are aggregates of smaller particles (**Figure 8**).

Based on the obtained big iron content in studied PM, 57Fe Mössbauer spectroscopy was applied to investigate samples. It allows to go deeper into the PM characteristics and to make more clear conclusions about the presented iron-bearing chemical compounds, their quantity, and dispersion. Represented

**61**

*Long-Distance LIDAR Mapping Schematic for Fast Monitoring of Bioaerosol Pollution over Large…*

*EDS spectrum of element analysis and the insert presents SEM image of the element's distribution on the fixed* 

Mössbauer spectra of samples can be seen on **Figure 11**. Sextet and doublet components were obtained after spectrum evaluation. They have the characteristic parameters of phases presented on the respective figures (**Figure 11A** and **B**). The main differences between studied PM from IT and GA could be regarded to quantity of presented phases and their particle size. After spectrum evaluation the sextet components with hyperfine parameters characteristic for spinel phase were resolved, highly non-stoichiometric magnetite Fe3−xO4 phase (or maghemite phase γ-Fe2O3) in all studied samples. Their quantity is much bigger in high-traffic area than in residence green area (29% vs. 12%). On the other hand, the calculated values of hyperfine effective fields of all registered magnetic phases (both spinel and hematite phase) are lower than the typical ones for the respective bulk phases. So it can be concluded that the particle size of these oxide phases is lower than 20 nm [26, 27]. Hematite phase dispersion is almost the same in IT and GA samples, but maghemite/magnetite phase particle size is smaller in IT area. It can be well seen on **Figure 11** that the doublet components are the main part of all PM spectra. According to the calculated hyperfine parameters of these doublets and comparison with previous investigations, it can be concluded that the majority of Fe-bearing compounds in IT PM are superparamagnetic (SPM) phases with nanometric size

*Representative X-ray diffraction patterns of studied powder PM originated from IT (A) and GA (B).*

*DOI: http://dx.doi.org/10.5772/intechopen.87031*

*particle adsorbed on the filters from IT zone (23.03.2019).*

**Figure 9.**

**Figure 10.**

*Long-Distance LIDAR Mapping Schematic for Fast Monitoring of Bioaerosol Pollution over Large… DOI: http://dx.doi.org/10.5772/intechopen.87031*

#### **Figure 9.**

*Atmospheric Air Pollution and Monitoring*

**Figure 7.**

**Figure 8.**

*(annual average of mg/m3*

patterns of studied PM samples originated from different areas (IT and GA). The existence of low intensity and broad X-ray diffraction peaks laid on nonselective background in all registered patterns was observed. The main crystallite phases detected in X-ray diffractograms are silicates and aluminosilicates and carbonates. Lesser amounts of different sulfates were found also. Registered X-ray amorphous halos and nonselective background together with small intensity and high width of diffraction peaks of all registered crystallite phases indicate nearly amorphous structure, small particle size, and low crystallinity degree of PM material from both studied locations. The observations are in good consideration with obtained elemental composition and SEM analysis of studied materials. Regarding both analyses it could be concluded that PM greatly vary in size from nanometers to several tenths of micrometers. However, most of the particles on SEM images are

*EDS spectrum of element analysis and the insert presents SEM image of the element's distribution on the fixed* 

*Metals with concentrations exceeding the permissible limiting values (LV); the blue bars indicate the LV* 

*traffic (IT) and a green area (GA), respectively, as estimated by our LIDAR measurements.*

*), and the red and green bars indicate the average concentrations in an intensive* 

Based on the obtained big iron content in studied PM, 57Fe Mössbauer spectroscopy was applied to investigate samples. It allows to go deeper into the PM characteristics and to make more clear conclusions about the presented iron-bearing chemical compounds, their quantity, and dispersion. Represented

**60**

aggregates of smaller particles (**Figure 8**).

*particle adsorbed on the filters from IT zone (23.03.2019).*

*EDS spectrum of element analysis and the insert presents SEM image of the element's distribution on the fixed particle adsorbed on the filters from IT zone (23.03.2019).*

**Figure 10.**

*Representative X-ray diffraction patterns of studied powder PM originated from IT (A) and GA (B).*

Mössbauer spectra of samples can be seen on **Figure 11**. Sextet and doublet components were obtained after spectrum evaluation. They have the characteristic parameters of phases presented on the respective figures (**Figure 11A** and **B**). The main differences between studied PM from IT and GA could be regarded to quantity of presented phases and their particle size. After spectrum evaluation the sextet components with hyperfine parameters characteristic for spinel phase were resolved, highly non-stoichiometric magnetite Fe3−xO4 phase (or maghemite phase γ-Fe2O3) in all studied samples. Their quantity is much bigger in high-traffic area than in residence green area (29% vs. 12%). On the other hand, the calculated values of hyperfine effective fields of all registered magnetic phases (both spinel and hematite phase) are lower than the typical ones for the respective bulk phases. So it can be concluded that the particle size of these oxide phases is lower than 20 nm [26, 27]. Hematite phase dispersion is almost the same in IT and GA samples, but maghemite/magnetite phase particle size is smaller in IT area. It can be well seen on **Figure 11** that the doublet components are the main part of all PM spectra. According to the calculated hyperfine parameters of these doublets and comparison with previous investigations, it can be concluded that the majority of Fe-bearing compounds in IT PM are superparamagnetic (SPM) phases with nanometric size

#### **Figure 11.**

*Representative Mössbauer spectra of studied powder PM originated from IT (A) and GA (B)*

(oxides or hydroxides). Fe3+ in paramagnetic phases as glass phases, sulfates, clay minerals, etc. is also presented in both locations (also in this component) [28]. Fe2+ was found in paramagnetic component indicative for the presence of aluminosilicate glass, ankerite, iron-containing carbonates or clay minerals, etc. [29]. The last component is the largest component in GA Möessbauer spectrum (see **Figure 11B**).

Comparative analysis of registered FTIR spectra of PM reveals appearance and increasing of bands typical for the organic compounds and inorganic salts (mainly sulfates and phosphates) typically presented on the surface of studied particles [30]. Obtained results indicate that silicates contributed to the highest percentage of total analyzed IR spectra signal for particles at both locations. Organic compounds are hydrocarbons and substituted hydrocarbons, adsorbed CO, etc. They could be regarded to partially oxidized products of fuel combustion. Registered X-ray photoelectron spectra of samples gave additional information for presented surface elements and their concentrations and chemical state. C, O, Si, Al, Ca, N, Na, and S have been detected on the PM surface. The determined binding energies are typical for Si-O, Al-Si-O, Ca-CO3, C-H-N, C-H-NOx, C-H-N-Cl, Na-C-H-N-Cl, Cu-Cl-C-N-H-O, and Cu-O bonding, respectively [31]. Trace levels of Fe, Cu, Zn, and Cl were also found, and this could be related to automobile exhaust emissions and to brakes and tire wear. So, the analysis shows that the surface of investigated PM sample consists mainly of silicate and aluminosilicate compounds, as well as of different organic and inorganic carbon phases/carbonaceous species. XPS showed the presence of carbon black, which was attributed to the oxidative wear and subsequent deposition from related volatiles, as well as of graphitic particles emitted as a result of abrasive wear. The last one was registered in XRD patterns also. According to the literature data, most of the surface carbon comes from burning of fossil fuels, which is not surprising as all the samples were collected in winter period [32].

The analysis of obtained results using different characterization methods— XRD, Mössbauer, IR, and XPS—allows us to make conclusions about chemical composition of studied airborne PM, as well as about the quantity, crystallinity degree, and dispersion of compounds. The main phases registered in samples from both IT and GA are silicate, aluminosilicate, clay minerals, and sulfate compounds, as well as organic and inorganic (carbonate and coal) carbon phases. Elemental analysis showed that the Fe is the dominated metallic content in high-traffic area. The obtained bigger than usual content of iron in PM could be regarded to the

**63**

*Long-Distance LIDAR Mapping Schematic for Fast Monitoring of Bioaerosol Pollution over Large…*

airborne particles produced by transport and mainly by car engine performance [33]. This also explained the observation of higher iron content in IT samples compared to those from GA. On the other hand, Fe, Cu, Zn, Pb, and S in relatively high concentrations in all measured airborne PM (see **Figure 7**) could be regarded to abrasion of car brake lining material. Although the Pb is having been replaced in modern brake linings, the presence of cars older than 15–20 years is probably the explanation of registered Pb content in studied PM. This is an important issue for further regulations. Smaller quantity of other metals as Ba, Mg, Ni, and K has been found, which also could be considered as a result of car lining wear. Fe domination among trace elements was attributed to the easiest fragmentation of Cu, Zn, and Sn due to their lower mechanical properties, as well as their lower melting points, compared to steel and cast iron. Therefore, car braking could be considered to be one of the major sources of nonexhaust traffic-related emissions in all urban locations. The main source of silicate and aluminosilicate compounds could be considered to be mostly natural (silicate, aluminosilicate, clay minerals). The anthropogenic factors in PM formation are connected with street and house reparation activities. The noticeable concentrations of organic substances and elemental carbon have been recognized as a result of incomplete fuel combustion, lubricant volatilization during the combustion procedure, and residuals from the exhaust gases originating from power plants, small houses and different engines,

Bioaerosols may contain pollen, bacteria, actinomycetes, fungal spores, and sneezing and cough drops, as well as endotoxins, mycotoxins, and allergens. A number of studies have shown that bacteria in the air most commonly coexist with particulate matter and are thus transported over long distances [38, 39]. The average residence time of bioaerosols in the atmosphere may be from day to several weeks, depending on their size and aerodynamic properties [40]. Larger bioaerosol particles are retained in the upper airways of the human (oral and nasal cavities), while smaller ones can reach the lower pathways in the lungs [41, 42]. They can have a different negative effect on humans (infectious diseases, toxic effects, allergies, and even cancers). Most commonly, the symptoms and diseases resulting from the inhalation of bioaerosols are related to the respiratory system. Causes of some human infections, such as measles or tuberculosis, can spread through bioaerosols containing infectious microorganisms [43, 44]. Fungal spores, as part of bioaerosol particles, are most often associated with asthmatic symptoms and are a risk factor for various respiratory problems. Pulmonary plague caused by *Yersinia pestis* can spread after inhalation of bioaerosol particles containing the pathogen. Qualitative and quantitative composition of microorganisms varies greatly [2]. In the air over Erdemli, Turkey, during the passage of Saharan dust in March 2002, bacteria belonging to seven genera were isolated, and the majority of the species were referred to the genus *Streptomyces* [45]. In Bamako, Mali, representatives of 20 genera were identified during the passage of a large amount of desert dust, with *Bacillus* species representing 38% of all isolates, followed by genera *Kocuria* (12.8%), *Saccharococcus* (7.4%), and *Micrococcu*s (6.4%). From the 95 species of bacteria identified in this study, about 10% are potential pathogens in animals, 5%

are phytopathogens, and 25% are opportunistic human pathogens [46]. Eukaryotic microorganisms from genera *Cladosporium*, *Alternaria*, and *Epicoccum* are the dominant species found in open air in different parts of the world, while species of the genera *Penicillium* and *Aspergillus* are more often isolated from enclosed spaces [47]. Saharan sandstorms are responsible for the

*DOI: http://dx.doi.org/10.5772/intechopen.87031*

road surface wear, etc. [34–37].

**3.3 Bioaerosols**

*Long-Distance LIDAR Mapping Schematic for Fast Monitoring of Bioaerosol Pollution over Large… DOI: http://dx.doi.org/10.5772/intechopen.87031*

airborne particles produced by transport and mainly by car engine performance [33]. This also explained the observation of higher iron content in IT samples compared to those from GA. On the other hand, Fe, Cu, Zn, Pb, and S in relatively high concentrations in all measured airborne PM (see **Figure 7**) could be regarded to abrasion of car brake lining material. Although the Pb is having been replaced in modern brake linings, the presence of cars older than 15–20 years is probably the explanation of registered Pb content in studied PM. This is an important issue for further regulations. Smaller quantity of other metals as Ba, Mg, Ni, and K has been found, which also could be considered as a result of car lining wear. Fe domination among trace elements was attributed to the easiest fragmentation of Cu, Zn, and Sn due to their lower mechanical properties, as well as their lower melting points, compared to steel and cast iron. Therefore, car braking could be considered to be one of the major sources of nonexhaust traffic-related emissions in all urban locations. The main source of silicate and aluminosilicate compounds could be considered to be mostly natural (silicate, aluminosilicate, clay minerals). The anthropogenic factors in PM formation are connected with street and house reparation activities. The noticeable concentrations of organic substances and elemental carbon have been recognized as a result of incomplete fuel combustion, lubricant volatilization during the combustion procedure, and residuals from the exhaust gases originating from power plants, small houses and different engines, road surface wear, etc. [34–37].

#### **3.3 Bioaerosols**

*Atmospheric Air Pollution and Monitoring*

**Figure 11.**

(oxides or hydroxides). Fe3+ in paramagnetic phases as glass phases, sulfates, clay minerals, etc. is also presented in both locations (also in this component) [28]. Fe2+ was found in paramagnetic component indicative for the presence of aluminosilicate glass, ankerite, iron-containing carbonates or clay minerals, etc. [29]. The last component is the largest component in GA Möessbauer spectrum (see **Figure 11B**). Comparative analysis of registered FTIR spectra of PM reveals appearance and increasing of bands typical for the organic compounds and inorganic salts (mainly sulfates and phosphates) typically presented on the surface of studied particles [30]. Obtained results indicate that silicates contributed to the highest percentage of total analyzed IR spectra signal for particles at both locations. Organic compounds are hydrocarbons and substituted hydrocarbons, adsorbed CO, etc. They could be regarded to partially oxidized products of fuel combustion. Registered X-ray photoelectron spectra of samples gave additional information for presented surface elements and their concentrations and chemical state. C, O, Si, Al, Ca, N, Na, and S have been detected on the PM surface. The determined binding energies are typical for Si-O, Al-Si-O, Ca-CO3, C-H-N, C-H-NOx, C-H-N-Cl, Na-C-H-N-Cl, Cu-Cl-C-N-H-O, and Cu-O bonding, respectively [31]. Trace levels of Fe, Cu, Zn, and Cl were also found, and this could be related to automobile exhaust emissions and to brakes and tire wear. So, the analysis shows that the surface of investigated PM sample consists mainly of silicate and aluminosilicate compounds, as well as of different organic and inorganic carbon phases/carbonaceous species. XPS showed the presence of carbon black, which was attributed to the oxidative wear and subsequent deposition from related volatiles, as well as of graphitic particles emitted as a result of abrasive wear. The last one was registered in XRD patterns also. According to the literature data, most of the surface carbon comes from burning of fossil fuels, which is not surprising as all the samples were collected in winter period [32]. The analysis of obtained results using different characterization methods— XRD, Mössbauer, IR, and XPS—allows us to make conclusions about chemical composition of studied airborne PM, as well as about the quantity, crystallinity degree, and dispersion of compounds. The main phases registered in samples from both IT and GA are silicate, aluminosilicate, clay minerals, and sulfate compounds, as well as organic and inorganic (carbonate and coal) carbon phases. Elemental analysis showed that the Fe is the dominated metallic content in high-traffic area. The obtained bigger than usual content of iron in PM could be regarded to the

*Representative Mössbauer spectra of studied powder PM originated from IT (A) and GA (B)*

**62**

Bioaerosols may contain pollen, bacteria, actinomycetes, fungal spores, and sneezing and cough drops, as well as endotoxins, mycotoxins, and allergens. A number of studies have shown that bacteria in the air most commonly coexist with particulate matter and are thus transported over long distances [38, 39]. The average residence time of bioaerosols in the atmosphere may be from day to several weeks, depending on their size and aerodynamic properties [40]. Larger bioaerosol particles are retained in the upper airways of the human (oral and nasal cavities), while smaller ones can reach the lower pathways in the lungs [41, 42]. They can have a different negative effect on humans (infectious diseases, toxic effects, allergies, and even cancers). Most commonly, the symptoms and diseases resulting from the inhalation of bioaerosols are related to the respiratory system. Causes of some human infections, such as measles or tuberculosis, can spread through bioaerosols containing infectious microorganisms [43, 44]. Fungal spores, as part of bioaerosol particles, are most often associated with asthmatic symptoms and are a risk factor for various respiratory problems. Pulmonary plague caused by *Yersinia pestis* can spread after inhalation of bioaerosol particles containing the pathogen. Qualitative and quantitative composition of microorganisms varies greatly [2]. In the air over Erdemli, Turkey, during the passage of Saharan dust in March 2002, bacteria belonging to seven genera were isolated, and the majority of the species were referred to the genus *Streptomyces* [45]. In Bamako, Mali, representatives of 20 genera were identified during the passage of a large amount of desert dust, with *Bacillus* species representing 38% of all isolates, followed by genera *Kocuria* (12.8%), *Saccharococcus* (7.4%), and *Micrococcu*s (6.4%). From the 95 species of bacteria identified in this study, about 10% are potential pathogens in animals, 5% are phytopathogens, and 25% are opportunistic human pathogens [46].

Eukaryotic microorganisms from genera *Cladosporium*, *Alternaria*, and *Epicoccum* are the dominant species found in open air in different parts of the world, while species of the genera *Penicillium* and *Aspergillus* are more often isolated from enclosed spaces [47]. Saharan sandstorms are responsible for the transmission of pathogens associated with widespread coral infections (predominantly *Aspergillus* genus) in the Caribbean region [48–50].

The concentration of bioaerosol particles varies greatly depending on the weather, location, and annual seasons. The wind, rain, solar radiation, and ozone are factors that influence the concentration of microorganisms in the air and may even have a bactericidal effect. The survival of bacteria in the air decreases with increasing temperatures as it begins to decrease when temperatures exceed 24°C [44–52]. High relative humidity (RH) can significantly reduce the bactericidal effect of ultraviolet light and hence increase the survival of bacteria [53]. Quantitative composition also influences some pollutants in the air, for example, formaldehyde, acrolein, ozone, and sulfur dioxide, which have a negative effect on the viability of the bacteria [54]. Due to the strong influence of these factors, the quantitative composition of the air microbiota is unstable and depends on local sources of pollution. Statistics based on the results of the US Environmental Protection Agency study show that bacterial concentration in open spaces is higher than in indoor pools [55]. The average concentration of all bacteria isolated from outdoor air is 102 CFU/m3 . Ninety-five percent of the culturable bacteria are mesophilic. In a study conducted in the United States, it has been found that the concentration of fungi is usually higher in open spaces than in the indoor. The highest concentration of fungi is measured in autumn and summer and the lowest in winter and spring. In open spaces, the concentration of fungi varies strongly from 1 to 8200 CFU/m3 of air, with an average count of about 540 CFU/m3 [56].

Fungi and their spores are more resistant to stress in the air environment than viruses and vegetative cells of the bacteria [44, 52]. Higher temperatures, wet substrates, and humidity provide favorable conditions for fungal development [57]. The quantitative analysis of the microbiome of air bioaerosols in the points tested reviled the presence of aerobic heterotrophic and oligotrophic microorganisms and fungi (**Tables 1** and **2**).

The analysis of the results obtained show that the quantities of the heterotrophic and oligotrophic microorganisms in the air of the first location mentioned are significantly higher. It is also obvious that the levels of these microorganisms in both points had a tendency to decrease during warm summer period, which correlated with lower urban traffic at this period. It must be noted that the quantity of fungi in the second location is significantly higher than the detected levels in the first one, but there is a tendency to increase during spring-summer period. The results are similar to those found by other authors [45, 46, 58–60]. Fifty-six pure cultures isolated from both points tested subjected to taxonomic investigation. More than 45% of the isolates are Gram-positive and belong to genus *Bacillus* (*B. cereus*, *B. pumilus*, *B. subtilis*, *B. megaterium*, *B. thuringiensis*, and *B. mycoides* are the dominant species). Information about the prevalence of such bacteria in similar sampling locations is available in the literature [39, 58, 60, 61].


**65**

aerosols.

**Table 2.**

**4. Conclusions**

*Long-Distance LIDAR Mapping Schematic for Fast Monitoring of Bioaerosol Pollution over Large…*

**Sampling May**

**CFU/m3**

**Sampling August**

**Sampling November**

**Microorganisms Quantity of detected microorganisms**

**Sampling January**

*Quantitative analysis of culturable microorganisms in the air of the point green area.*

The molecular analysis and sequencing confirmed these results. *Erwinia herbicola* was the dominant species from family *Enterobacteriaceae*. *Bacillus megaterium* and *Bacillus pumilus* as well as *Rathayibacter caricis*, *Arthrobacter* sp. *FXJ8.160*, *Acidovorax* sp. *NA2*, *Plantibacter flavus*, and *Kocuria rosea* were most frequently isolated. It can be summarized that from the group of Gram-positive bacteria in both locations, 48% of the isolates were related to *Bacillus* genus; 21% of all isolates were related to the *Enterobacteriaceae* family; 10% were related to the *Arthrobacter* genus; 4% were related to the genus *Exiguobacteria*; and 2% of the isolated microorganisms were related to the genera *Staphylococcus*, *Acidovorax*, *Plantibacter*, *Gordonia*, *Streptomyces*, *Kocuria*, and *Rathayibacter*. It is noteworthy that contingent pathogens (*Bacillus cereus*, *Bacillus pumilus*, *Erwinia herbicola*, *Enterobacter aerogenes*) are found among the isolates and such pathogens are also found in other studies of airborne microbial load [45]. Representatives of the genus *Gordonia* are conditionally pathogenic and isolated from sick patients. Representatives of the genus *Kocuria* are part of the resident microflora of the skin and mouth in humans but are also widespread in all elements of the environment. Representatives of the

Heterotrophic MO 9741 ± 1.1 6182 ± 2.1 5932 ± 1.8 83,462 ± 1.3 Oligotrophic MO 9086 ± 1.9 4777 ± 1.1 5323 ± 2.2 10,321 ± 2.1 Fungi 2248 ± 1.7 8992 ± 1.3 6332 ± 0.9 4672 ± 1.1

Fungal isolates from both points investigated belong mainly to genera *Aspergillus* (*Aspergillus fumigatus*, *Aspergillus versicolor*), *Penicillium* (*P. sanguifluum*, *P. chrysogenum*, *P. brevicompactum*), *Cladosporium* (*C. sphaerospermum*), *Botrytis* (*B. cinerea*), and *Symmetrospora*. Some of these fungi isolated are typical human pathogens [47–49]. All these findings confirmed the idea that the investigations on particular matters (PM) must combine obligatory with analyzing of the microbiota in air

The LIDAR monitoring methodology discussed opens up possibilities for rapid space-time identification and characterization of physicochemical and bio-pollution processes over the entire territory of large cities without affecting the normal living patterns of the city residents. Remote sensing allows one to locate the occurrence of pollution, which can be easily combined with in situ sampling. The experimental results described above clearly showed that at points of extreme pollution, the small-size particles (less than 2.5 μm) predominated in areas of heavy traffic, while nanosized PM were also found. Further, in intensive traffic areas, we observed metal particles, whose concentrations exceeded the maximal admissible levels. LIDAR sounding of "greener" areas revealed the predominant presence of particles with sizes ≥10 μm; the analyses conducted indicated their prevailing biological origin. The LIDAR maps created can be further used for tracing the full air-mass transport, carrying contamination from a number of pollution sources

genus *Rathayibacter* cause serious damage to the nervous system.

*DOI: http://dx.doi.org/10.5772/intechopen.87031*

#### **Table 1.**

*Quantitative analysis of culturable microorganisms in the air of the intense traffic.*

*Long-Distance LIDAR Mapping Schematic for Fast Monitoring of Bioaerosol Pollution over Large… DOI: http://dx.doi.org/10.5772/intechopen.87031*


#### **Table 2.**

*Atmospheric Air Pollution and Monitoring*

outdoor air is 102

8200 CFU/m3

fungi (**Tables 1** and **2**).

CFU/m3

available in the literature [39, 58, 60, 61].

transmission of pathogens associated with widespread coral infections (predomi-

The concentration of bioaerosol particles varies greatly depending on the weather, location, and annual seasons. The wind, rain, solar radiation, and ozone are factors that influence the concentration of microorganisms in the air and may even have a bactericidal effect. The survival of bacteria in the air decreases with increasing temperatures as it begins to decrease when temperatures exceed 24°C [44–52]. High relative humidity (RH) can significantly reduce the bactericidal effect of ultraviolet light and hence increase the survival of bacteria [53]. Quantitative composition also influences some pollutants in the air, for example, formaldehyde, acrolein, ozone, and sulfur dioxide, which have a negative effect on the viability of the bacteria [54]. Due to the strong influence of these factors, the quantitative composition of the air microbiota is unstable and depends on local sources of pollution. Statistics based on the results of the US Environmental Protection Agency study show that bacterial concentration in open spaces is higher than in indoor pools [55]. The average concentration of all bacteria isolated from

philic. In a study conducted in the United States, it has been found that the concentration of fungi is usually higher in open spaces than in the indoor. The highest concentration of fungi is measured in autumn and summer and the lowest in winter and spring. In open spaces, the concentration of fungi varies strongly from 1 to

of air, with an average count of about 540 CFU/m3

**Microorganisms Quantity of the detected microorganisms**

**Sampling January**

*Quantitative analysis of culturable microorganisms in the air of the intense traffic.*

Fungi and their spores are more resistant to stress in the air environment than viruses and vegetative cells of the bacteria [44, 52]. Higher temperatures, wet substrates, and humidity provide favorable conditions for fungal development [57]. The quantitative analysis of the microbiome of air bioaerosols in the points tested reviled the presence of aerobic heterotrophic and oligotrophic microorganisms and

The analysis of the results obtained show that the quantities of the heterotrophic and oligotrophic microorganisms in the air of the first location mentioned are significantly higher. It is also obvious that the levels of these microorganisms in both points had a tendency to decrease during warm summer period, which correlated with lower urban traffic at this period. It must be noted that the quantity of fungi in the second location is significantly higher than the detected levels in the first one, but there is a tendency to increase during spring-summer period. The results are similar to those found by other authors [45, 46, 58–60]. Fifty-six pure cultures isolated from both points tested subjected to taxonomic investigation. More than 45% of the isolates are Gram-positive and belong to genus *Bacillus* (*B. cereus*, *B. pumilus*, *B. subtilis*, *B. megaterium*, *B. thuringiensis*, and *B. mycoides* are the dominant species). Information about the prevalence of such bacteria in similar sampling locations is

. Ninety-five percent of the culturable bacteria are meso-

**CFU/m3**

**Sampling August**

**Sampling November**

**Sampling May**

Heterotrophic MO 64,256 ± 2.1 36,343 ± 2.4 32,147 ± 3.1 50,382 ± 1.5 Oligotrophic MO 56,950 ± 2.5 29,412 ± 1.8 27,876 ± 1.1 52,356 ± 2.3 Fungi 3747 ± 1.1 5223 ± 2.1 5322 ± 1.7 3245 ± 1.2

[56].

nantly *Aspergillus* genus) in the Caribbean region [48–50].

**64**

**Table 1.**

*Quantitative analysis of culturable microorganisms in the air of the point green area.*

The molecular analysis and sequencing confirmed these results. *Erwinia herbicola* was the dominant species from family *Enterobacteriaceae*. *Bacillus megaterium* and *Bacillus pumilus* as well as *Rathayibacter caricis*, *Arthrobacter* sp. *FXJ8.160*, *Acidovorax* sp. *NA2*, *Plantibacter flavus*, and *Kocuria rosea* were most frequently isolated. It can be summarized that from the group of Gram-positive bacteria in both locations, 48% of the isolates were related to *Bacillus* genus; 21% of all isolates were related to the *Enterobacteriaceae* family; 10% were related to the *Arthrobacter* genus; 4% were related to the genus *Exiguobacteria*; and 2% of the isolated microorganisms were related to the genera *Staphylococcus*, *Acidovorax*, *Plantibacter*, *Gordonia*, *Streptomyces*, *Kocuria*, and *Rathayibacter*. It is noteworthy that contingent pathogens (*Bacillus cereus*, *Bacillus pumilus*, *Erwinia herbicola*, *Enterobacter aerogenes*) are found among the isolates and such pathogens are also found in other studies of airborne microbial load [45]. Representatives of the genus *Gordonia* are conditionally pathogenic and isolated from sick patients. Representatives of the genus *Kocuria* are part of the resident microflora of the skin and mouth in humans but are also widespread in all elements of the environment. Representatives of the genus *Rathayibacter* cause serious damage to the nervous system.

Fungal isolates from both points investigated belong mainly to genera *Aspergillus* (*Aspergillus fumigatus*, *Aspergillus versicolor*), *Penicillium* (*P. sanguifluum*, *P. chrysogenum*, *P. brevicompactum*), *Cladosporium* (*C. sphaerospermum*), *Botrytis* (*B. cinerea*), and *Symmetrospora*. Some of these fungi isolated are typical human pathogens [47–49]. All these findings confirmed the idea that the investigations on particular matters (PM) must combine obligatory with analyzing of the microbiota in air aerosols.

## **4. Conclusions**

The LIDAR monitoring methodology discussed opens up possibilities for rapid space-time identification and characterization of physicochemical and bio-pollution processes over the entire territory of large cities without affecting the normal living patterns of the city residents. Remote sensing allows one to locate the occurrence of pollution, which can be easily combined with in situ sampling. The experimental results described above clearly showed that at points of extreme pollution, the small-size particles (less than 2.5 μm) predominated in areas of heavy traffic, while nanosized PM were also found. Further, in intensive traffic areas, we observed metal particles, whose concentrations exceeded the maximal admissible levels. LIDAR sounding of "greener" areas revealed the predominant presence of particles with sizes ≥10 μm; the analyses conducted indicated their prevailing biological origin. The LIDAR maps created can be further used for tracing the full air-mass transport, carrying contamination from a number of pollution sources

(chemical, biological, dust, etc.) distributed over the scanned region. Finally, we should emphasize the simplicity of the LIDAR and the aerosol sampling equipment used and, thus, the possibilities for its wide use in any populated region, where keeping the air quality within tolerable levels is problematic. We should once again note that methodology developed affects negligibly the residents' lifestyle in urban regions. Therefore, it is our belief that it offers promising paths for wide application in air-quality monitoring in highly polluted large urban areas.

## **Acknowledgements**

This work was financed in part by contract DН18/16 with the National Science Fund, Bulgaria, and included in the European Program of the COST Action CA16202. The scanning LIDAR system was developed as part of the EARLINET and ACTRIS-2, Horizon 2020 EU projects.

## **Author details**

Dimitar Stoyanov1 , Ivan Nedkov1 \*, Veneta Groudeva2 , Zara Cherkezova-Zheleva3 , Ivan Grigorov1 , Georgy Kolarov1 , Mihail Iliev2 , Ralitsa Ilieva2 , Daniela Paneva<sup>2</sup> and Chavdar Ghelev1

1 Institute of Electronics, Bulgarian Academy of Sciences, Sofia, Bulgaria

2 Faculty of Biology, St. Kliment Ohridski University of Sofia, Sofia, Bulgaria

3 Institute of Catalysis, Bulgarian Academy of Sciences, Sofia, Bulgaria

\*Address all correspondence to: nedkovivan@yahoo.co.uk

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**67**

*Long-Distance LIDAR Mapping Schematic for Fast Monitoring of Bioaerosol Pollution over Large…*

org/title/laser-remote-sensingfundamentals-and-applications/

[9] Kovalev VA, Eichinger WE. Elastic LIDAR: Theory, Practice, and Analysis Methods. 1st ed. NY, USA: Wiley&Sons; 2004. 615 p. DOI: 10.1002/0471643173

[10] Weitkamp C, editor. LIDAR Range-Resolved Optical Remote Sensing of the Atmosphere. Springer Series in Optical Sciences, Springer; 2005. 456 p. DOI:

[11] Stoyanov D, Dreischuh T, Grigorov I, Kolarov G, Deleva A, Peshev Z, et al. Near surface aerosol LIDAR mapping of Sofia Area. On the synergy with city sensor network. In: Proceedings of the Final Meeting—Sixth Sci. Meeting EuNetAir. 2016. pp. 61-64. DOI: 10.5162/6 EuNetAir2016/16

[12] Simard JR, Roy G, Mathieu P, Larochelle V, McFee J, Ho J. Standoff

[13] Simard JR, Roy G, Mathieu P, Larochelle V, McFee J, Ho J. Standoff sensing of bioaerosols using intensified

range-gated spectral analysis of laser induced fluorescence. IEEE

10.1109/TGRS.2003.823285

Transactions on Geoscience and Remote Sensing. 2004;**42**(4):865-874. DOI:

[14] He TY, Stanič S, Gao F, Bergant K, Veberič D, Song Q, et al. Tracking of urban aerosols using combined LIDARbased remote sensing and groundbased measurements. Atmospheric Measurement Techniques. 2012;**5**: 891-900. DOI: 10.5194/amt-5-891-2012

[15] Peshev ZY, Dreischuh TN, Toncheva EN, Stoyanov DV. Two-wavelength

Hyperspectral Detection (SINBAHD): Final Report. 2002: DREV-TR-2002-125.

Integrated bioaerosol Active

DOI: pubs.drdc-rddc.gc.ca/ BASIS/pcandid/www/engpub/ DDW?W%3DSYSNUM=518849

oclc/123159913

10.1007/b106786

*DOI: http://dx.doi.org/10.5772/intechopen.87031*

[1] Fuzzi S, Baltensperger U, Carslaw K, Decesari S, Denier van der Gon H, Facchini MC, et al. Particulate matter, air quality and climate: Lessons learned and future needs. Atmospheric Chemistry and Physics. 2015;**15**:8217-8299. DOI: 10.5194/acp-15-8217-2015

[2] Brodie EL, De-Santis TZ, Moberg Parker JP, Zubietta IX, Piceno YM, Andersen GL. Urban aerosols harbor diverse and dynamic bacterial populations. Proceedings of the National Academy of Sciences of the United States of America. 2007;**104**(1):299-304. DOI: 10.1073/

[3] Jones AM, Harrison RM. The effects of meteorological factors on atmospheric bioaerosol concentrations—A review. Science of the Total Environment. 2004;**326**(1-3):151-180. DOI: 10.1016/j.

[4] Macher J. Bioaerosols: Assessment and Control, American Conference of Governmental Industrial Hygienists. USA: OH Cincinnati; 1999. 322 p. ISBN:

[5] Stanley RG, Linskins HF. Pollen: Biology, Chemistry and Management. 1st ed. Berlin, Germany: Springer Verlag; 1974. DOI: https://www. springer.com/gp/book/9783642659072

[6] Gregory PH. The Microbiology of the Atmosphere. 2nd ed. London: Hall; 1973. 377 p. DOI: https://catalogue.nla.

[7] Maricovich H, editor. Black's Medical Dictionary. 42nd ed. USA: A&C Black; 2009. p. 765. ISBN-10:9780713689020

[8] Measures RM. Laser Remote Sensing: Fundamentals and Applications. 1st ed. NY, USA: Wiley&Sons; 1984. 510 p. DOI: https://www.worldcat.

**References**

pnas.0608255104

scitotenv.2003.11.021

978-1-882417-29-1

gov.au/Record/395639

*Long-Distance LIDAR Mapping Schematic for Fast Monitoring of Bioaerosol Pollution over Large… DOI: http://dx.doi.org/10.5772/intechopen.87031*

## **References**

*Atmospheric Air Pollution and Monitoring*

**Acknowledgements**

**Author details**

Dimitar Stoyanov1

and Chavdar Ghelev1

Ivan Grigorov1

, Ivan Nedkov1

, Georgy Kolarov1

provided the original work is properly cited.

ACTRIS-2, Horizon 2020 EU projects.

(chemical, biological, dust, etc.) distributed over the scanned region. Finally, we should emphasize the simplicity of the LIDAR and the aerosol sampling equipment used and, thus, the possibilities for its wide use in any populated region, where keeping the air quality within tolerable levels is problematic. We should once again note that methodology developed affects negligibly the residents' lifestyle in urban regions. Therefore, it is our belief that it offers promising paths for wide application

This work was financed in part by contract DН18/16 with the National Science

CA16202. The scanning LIDAR system was developed as part of the EARLINET and

\*, Veneta Groudeva2

, Ralitsa Ilieva2

, Mihail Iliev2

1 Institute of Electronics, Bulgarian Academy of Sciences, Sofia, Bulgaria

3 Institute of Catalysis, Bulgarian Academy of Sciences, Sofia, Bulgaria

\*Address all correspondence to: nedkovivan@yahoo.co.uk

2 Faculty of Biology, St. Kliment Ohridski University of Sofia, Sofia, Bulgaria

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

, Zara Cherkezova-Zheleva3

, Daniela Paneva<sup>2</sup>

,

Fund, Bulgaria, and included in the European Program of the COST Action

in air-quality monitoring in highly polluted large urban areas.

**66**

[1] Fuzzi S, Baltensperger U, Carslaw K, Decesari S, Denier van der Gon H, Facchini MC, et al. Particulate matter, air quality and climate: Lessons learned and future needs. Atmospheric Chemistry and Physics. 2015;**15**:8217-8299. DOI: 10.5194/acp-15-8217-2015

[2] Brodie EL, De-Santis TZ, Moberg Parker JP, Zubietta IX, Piceno YM, Andersen GL. Urban aerosols harbor diverse and dynamic bacterial populations. Proceedings of the National Academy of Sciences of the United States of America. 2007;**104**(1):299-304. DOI: 10.1073/ pnas.0608255104

[3] Jones AM, Harrison RM. The effects of meteorological factors on atmospheric bioaerosol concentrations—A review. Science of the Total Environment. 2004;**326**(1-3):151-180. DOI: 10.1016/j. scitotenv.2003.11.021

[4] Macher J. Bioaerosols: Assessment and Control, American Conference of Governmental Industrial Hygienists. USA: OH Cincinnati; 1999. 322 p. ISBN: 978-1-882417-29-1

[5] Stanley RG, Linskins HF. Pollen: Biology, Chemistry and Management. 1st ed. Berlin, Germany: Springer Verlag; 1974. DOI: https://www. springer.com/gp/book/9783642659072

[6] Gregory PH. The Microbiology of the Atmosphere. 2nd ed. London: Hall; 1973. 377 p. DOI: https://catalogue.nla. gov.au/Record/395639

[7] Maricovich H, editor. Black's Medical Dictionary. 42nd ed. USA: A&C Black; 2009. p. 765. ISBN-10:9780713689020

[8] Measures RM. Laser Remote Sensing: Fundamentals and Applications. 1st ed. NY, USA: Wiley&Sons; 1984. 510 p. DOI: https://www.worldcat.

org/title/laser-remote-sensingfundamentals-and-applications/ oclc/123159913

[9] Kovalev VA, Eichinger WE. Elastic LIDAR: Theory, Practice, and Analysis Methods. 1st ed. NY, USA: Wiley&Sons; 2004. 615 p. DOI: 10.1002/0471643173

[10] Weitkamp C, editor. LIDAR Range-Resolved Optical Remote Sensing of the Atmosphere. Springer Series in Optical Sciences, Springer; 2005. 456 p. DOI: 10.1007/b106786

[11] Stoyanov D, Dreischuh T, Grigorov I, Kolarov G, Deleva A, Peshev Z, et al. Near surface aerosol LIDAR mapping of Sofia Area. On the synergy with city sensor network. In: Proceedings of the Final Meeting—Sixth Sci. Meeting EuNetAir. 2016. pp. 61-64. DOI: 10.5162/6 EuNetAir2016/16

[12] Simard JR, Roy G, Mathieu P, Larochelle V, McFee J, Ho J. Standoff Integrated bioaerosol Active Hyperspectral Detection (SINBAHD): Final Report. 2002: DREV-TR-2002-125. DOI: pubs.drdc-rddc.gc.ca/ BASIS/pcandid/www/engpub/ DDW?W%3DSYSNUM=518849

[13] Simard JR, Roy G, Mathieu P, Larochelle V, McFee J, Ho J. Standoff sensing of bioaerosols using intensified range-gated spectral analysis of laser induced fluorescence. IEEE Transactions on Geoscience and Remote Sensing. 2004;**42**(4):865-874. DOI: 10.1109/TGRS.2003.823285

[14] He TY, Stanič S, Gao F, Bergant K, Veberič D, Song Q, et al. Tracking of urban aerosols using combined LIDARbased remote sensing and groundbased measurements. Atmospheric Measurement Techniques. 2012;**5**: 891-900. DOI: 10.5194/amt-5-891-2012

[15] Peshev ZY, Dreischuh TN, Toncheva EN, Stoyanov DV. Two-wavelength

LIDAR characterization of atmospheric aerosol fields at low altitudes over heterogeneous terrain. Journal of Applied Remote Sensing. 2012;**6**(1):063581. DOI: 10.1117/1. JRS.6.063581

[16] Klett J. Stable analytical inversion solution for processing LIDAR returns. Applied Optics. 1981;**20**(1):211-220. DOI: 10.1364/AO.20.000211

[17] Atlas KM. Handbook of Microbiological Media. 4th ed. Washington DC: ASM Press and Roca Raton, London, New York, USA: CRC Press; 2010. 2040 p. ISBN-10: 9781439804063

[18] Whitman W, editor. Bergey's Manual of Systematics of Archaea and Bacteria (BMSAB). Bergey's Manual Trust Publ; 2015. 2011 p. ISBN:9781118960608. DOI: 10.1002/978111896060

[19] Fernald F. Analysis of atmospheric LIDAR observations: Some comments. Applied Optics. 1984;**23**(5):652-653. DOI: 10.1364/AO.23.000652

[20] Wilson KH, Blitchington RB, Greene RC. Amplification of bacterial 16S ribosomal DNA with polymerase chain reaction. Journal of Clinical Microbiology. 1990;**28**(9):1942-1946. DOI: jcm.asm.org/content/28/9/1942

[21] White TJ, Bruns T, Lee S, Taylor JW. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In: Innis MA, Gelfand DH, Sninsky JJ, White TJ, editors. PCR Protocols: A Guide to Methods and Applications. New York.: Academic Press, Inc.; 1990. pp. 315-322. DOI: 10.1016/0307-4412(91)90165-5

[22] Fröhlich-Nowoisky DAP, Després VR, Pöschl U. High diversity of fungi in air particulate matter. Proceedings of the National Academy of Sciences of the United States of America.

2009;**106**(1):12814-12819. DOI: 10.1073/ pnas.0811003106

[23] Jaenicke R. Abundance of cellular material and proteins in the atmosphere. Science. 2005;**308**(5718):73. DOI: 10.1126/science.1106335

[24] Hind WC. Aerosol Technology: Properties, Behavior, and Measurement of Airborne Particles. 2nd ed. NY: John Wiley&Sons; 2012. 504 p. ISBN: 978-1-118-59197-0

[25] World Health Organization (WHO). Health Effects of Particulate Matter. Copenhagen: Regional office for Europe; 2013. ISBN: 978 92 890 0001 7

[26] Fock J, Hansen MF, Frandsen C, Mørup S. On the interpretation of Mössbauer spectra of magnetic nanoparticles. Journal of Magnetism and Magnetic Materials. 2018;**445**:11-21

[27] De Grave E, Van Alboom A. Evaluation of ferrous and ferric Mössbauer fractions. Physics and Chemistry of Minerals. 1991;**18**:337-342. DOI: 10.1007/BF00200191

[28] Mahieu B, Ladrière J, Desaedeleer G. Mössbauer spectroscopy of airborne particulate matter. Journal de Physique Colloques. 1976;**37**(C6):C6-837-C6-840. DOI: 10.1051/jphyscol:19766176

[29] Gietl J, Lawrence R, Thorpe A, Harrison R. Identification of brake wear particles and derivation of a quantitative tracer for brake dust at a major road. Atmospheric Environment. 2010;**44**:141-146. DOI: 10.1016/j. atmosenv.10.016

[30] Ji Z, Dai R, Zhang Z.

Characterization of fine particulate matter in ambient air by combining TEM and multiple spectroscopic techniques—NMR, FTIR and Raman spectroscopy. Environmental Science: Processes & Impacts. 2015;**17**:552-560. DOI: 10.1039/C4EM00678J

**69**

*Long-Distance LIDAR Mapping Schematic for Fast Monitoring of Bioaerosol Pollution over Large…*

particulate matter—A case study. Bulgarian Chemical Communications. 2018;**50F**:93-98. DOI: http://www.bcc.

[38] Schlesinger P, Mamane Y, Grishkan I. Transport of microorganisms to Israel during Saharan dust events. Aerobiologia. 2006;**22**:259-273. DOI:

bas.bg/index.html

10.1007/s10453-006-9038-7

[39] Maki T, Puspitasari F, Hara K, Yamada M, Kobayashi F, Hasegawa H, et al. Variations in the structure of airborne bacterial communities in a downwind area during an Asian dust (Kosa) event. Science of the Total Environment. 2014;**488-489**:75-84. DOI: 10.1016/j.scitotenv.2014.04.044

[40] De Nuntiis P, Maggi O, Mandrioli P, Ranalli G, Sorlini C. Monitoring the biological aerosol. In: Madrioli P, Caneva G, Sabbioni C, editors. Cultural Heritage and Aerobiology. Dordrecht: Kluwer Academic Publishers; 2003. pp. 107-144. ISBN: 978-94-017-0185-3

[41] Davies A, Thomson G, Walker J, Bennett A. A review of the risks and disease transmission associated with aerosol generating medical procedures. Journal of Infection Prevention. 2009;**10**(4):122-126. DOI:

[42] Després VR, Huffman JA, Burrows SM, Hoose C, Safatov AS, Buryak G, et al. Primary biological aerosol particles in the atmosphere: A review. Tellus B: Chemical and Physical Meteorology. 2012;**64**(1):2-20. DOI:

[43] Jones RM, Brosseau LM. Aerosol

[44] Ijaz MK, Zargar B, Wright KE, Rubino JR, Sattar SA. Generic aspects of the airborne spread of human

10.1177/1757177409106456

10.3402/tellusb.v64i0.15598

transmission of infectious disease. Journal of Occupational and Environmental Medicine. 2015;**57**(5):501-508. DOI: 10.1097/ JOM.0000000000000448

*DOI: http://dx.doi.org/10.5772/intechopen.87031*

[31] Moulder F, Sticke WF, Sobol PE, Bombel KD, Castain J, editors. Handbook of X-ray Photoelectron Spectroscopy. 2nd ed. Waltham, USA: Perkin-Elmer Corporation, Physical Electron Division; 1992. DOI: 10.1002/

[32] González L, Longoria-Rodríguez F, Sánchez-Domínguez M, Leyva-Porras C, Acuña-Askar K, Kharissov B, et al. Seasonal variation and chemical composition of particulate matter: A study by XPS, ICP-AES and sequential microanalysis using Raman with SEM/EDS. Journal of Environmental Sciences. 2018;**74**:32-49. DOI: 10.1016/j.

[33] Thorpe A, Harrison RM. Sources and properties of non-exhaust particulate matter from road traffic: A review. Science of the Total

Environment. 2008;**400**:270-282. DOI:

[34] Kelly F, Fussell J. Size, source and chemical composition as determinants of toxicity attributable to ambient particulate matter. Atmospheric Environment. 2012;**60**:504. DOI: 10.1016/j.atmosenv.2012.06.039

[35] Chen H, Laskin A, Baltrusaitis

[36] Kukutschová J, Moravec P, Tomásek V, Matejka V, Smolík J, Schwarz J, et al. On airborne nano/micro-sized wear particles released from low-metallic automotive brakes. Environmental Pollution. 2011;**159**:998-1006. DOI: 10.1016/j.envpol.2010.11.036

[37] Cherkezova-Zheleva Z, Paneva D, Kunev B, Kolev H, Shopska M, Nedkov I. Challenges at characterization of

J, Gorski CA, Scherer MM, Grassian VH. Coal fly ash as a source of iron in atmospheric dust. Environmental Science & Technology. 2012;**46**(4):2112-2120. DOI: https:// www.dora.lib4ri.ch/eawag/islandora/

object/eawag:7063

10.1016/j.scitotenv.2008.06.007

sia.740030412

jes.2018.02.002

*Long-Distance LIDAR Mapping Schematic for Fast Monitoring of Bioaerosol Pollution over Large… DOI: http://dx.doi.org/10.5772/intechopen.87031*

[31] Moulder F, Sticke WF, Sobol PE, Bombel KD, Castain J, editors. Handbook of X-ray Photoelectron Spectroscopy. 2nd ed. Waltham, USA: Perkin-Elmer Corporation, Physical Electron Division; 1992. DOI: 10.1002/ sia.740030412

*Atmospheric Air Pollution and Monitoring*

2009;**106**(1):12814-12819. DOI: 10.1073/

[23] Jaenicke R. Abundance of cellular material and proteins in the atmosphere. Science. 2005;**308**(5718):73. DOI:

[24] Hind WC. Aerosol Technology: Properties, Behavior, and Measurement of Airborne Particles. 2nd ed. NY: John Wiley&Sons; 2012. 504 p. ISBN:

[25] World Health Organization (WHO). Health Effects of Particulate Matter. Copenhagen: Regional office for Europe; 2013. ISBN: 978 92 890 0001 7

[26] Fock J, Hansen MF, Frandsen C, Mørup S. On the interpretation of Mössbauer spectra of magnetic nanoparticles. Journal of Magnetism and Magnetic Materials. 2018;**445**:11-21

[27] De Grave E, Van Alboom A. Evaluation of ferrous and ferric Mössbauer fractions. Physics and Chemistry of Minerals. 1991;**18**:337-342.

[28] Mahieu B, Ladrière J, Desaedeleer G. Mössbauer spectroscopy of airborne particulate matter. Journal de Physique Colloques. 1976;**37**(C6):C6-837-C6-840.

DOI: 10.1051/jphyscol:19766176

atmosenv.10.016

[30] Ji Z, Dai R, Zhang Z.

DOI: 10.1039/C4EM00678J

[29] Gietl J, Lawrence R, Thorpe A, Harrison R. Identification of brake wear particles and derivation of a quantitative tracer for brake dust at a major road. Atmospheric Environment. 2010;**44**:141-146. DOI: 10.1016/j.

Characterization of fine particulate matter in ambient air by combining TEM and multiple spectroscopic techniques—NMR, FTIR and Raman spectroscopy. Environmental Science: Processes & Impacts. 2015;**17**:552-560.

DOI: 10.1007/BF00200191

pnas.0811003106

10.1126/science.1106335

978-1-118-59197-0

[16] Klett J. Stable analytical inversion solution for processing LIDAR returns. Applied Optics. 1981;**20**(1):211-220.

LIDAR characterization of atmospheric aerosol fields at low altitudes over heterogeneous terrain. Journal of Applied Remote Sensing. 2012;**6**(1):063581. DOI: 10.1117/1.

DOI: 10.1364/AO.20.000211

[17] Atlas KM. Handbook of Microbiological Media. 4th ed. Washington DC: ASM Press and Roca Raton, London, New York, USA: CRC Press; 2010. 2040 p. ISBN-10:

[18] Whitman W, editor. Bergey's Manual of Systematics of Archaea and Bacteria (BMSAB). Bergey's Manual Trust Publ; 2015. 2011 p. ISBN:9781118960608. DOI: 10.1002/978111896060

[19] Fernald F. Analysis of atmospheric LIDAR observations: Some comments. Applied Optics. 1984;**23**(5):652-653.

DOI: 10.1364/AO.23.000652

[20] Wilson KH, Blitchington RB, Greene RC. Amplification of bacterial 16S ribosomal DNA with polymerase chain reaction. Journal of Clinical Microbiology. 1990;**28**(9):1942-1946. DOI: jcm.asm.org/content/28/9/1942

[21] White TJ, Bruns T, Lee S, Taylor JW. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In: Innis MA, Gelfand DH, Sninsky JJ, White TJ, editors. PCR Protocols: A Guide to Methods and Applications. New York.: Academic Press, Inc.; 1990. pp. 315-322. DOI: 10.1016/0307-4412(91)90165-5

[22] Fröhlich-Nowoisky DAP, Després VR, Pöschl U. High diversity of fungi in air particulate matter. Proceedings of the National Academy of Sciences of the United States of America.

JRS.6.063581

9781439804063

**68**

[32] González L, Longoria-Rodríguez F, Sánchez-Domínguez M, Leyva-Porras C, Acuña-Askar K, Kharissov B, et al. Seasonal variation and chemical composition of particulate matter: A study by XPS, ICP-AES and sequential microanalysis using Raman with SEM/EDS. Journal of Environmental Sciences. 2018;**74**:32-49. DOI: 10.1016/j. jes.2018.02.002

[33] Thorpe A, Harrison RM. Sources and properties of non-exhaust particulate matter from road traffic: A review. Science of the Total Environment. 2008;**400**:270-282. DOI: 10.1016/j.scitotenv.2008.06.007

[34] Kelly F, Fussell J. Size, source and chemical composition as determinants of toxicity attributable to ambient particulate matter. Atmospheric Environment. 2012;**60**:504. DOI: 10.1016/j.atmosenv.2012.06.039

[35] Chen H, Laskin A, Baltrusaitis J, Gorski CA, Scherer MM, Grassian VH. Coal fly ash as a source of iron in atmospheric dust. Environmental Science & Technology. 2012;**46**(4):2112-2120. DOI: https:// www.dora.lib4ri.ch/eawag/islandora/ object/eawag:7063

[36] Kukutschová J, Moravec P, Tomásek V, Matejka V, Smolík J, Schwarz J, et al. On airborne nano/micro-sized wear particles released from low-metallic automotive brakes. Environmental Pollution. 2011;**159**:998-1006. DOI: 10.1016/j.envpol.2010.11.036

[37] Cherkezova-Zheleva Z, Paneva D, Kunev B, Kolev H, Shopska M, Nedkov I. Challenges at characterization of

particulate matter—A case study. Bulgarian Chemical Communications. 2018;**50F**:93-98. DOI: http://www.bcc. bas.bg/index.html

[38] Schlesinger P, Mamane Y, Grishkan I. Transport of microorganisms to Israel during Saharan dust events. Aerobiologia. 2006;**22**:259-273. DOI: 10.1007/s10453-006-9038-7

[39] Maki T, Puspitasari F, Hara K, Yamada M, Kobayashi F, Hasegawa H, et al. Variations in the structure of airborne bacterial communities in a downwind area during an Asian dust (Kosa) event. Science of the Total Environment. 2014;**488-489**:75-84. DOI: 10.1016/j.scitotenv.2014.04.044

[40] De Nuntiis P, Maggi O, Mandrioli P, Ranalli G, Sorlini C. Monitoring the biological aerosol. In: Madrioli P, Caneva G, Sabbioni C, editors. Cultural Heritage and Aerobiology. Dordrecht: Kluwer Academic Publishers; 2003. pp. 107-144. ISBN: 978-94-017-0185-3

[41] Davies A, Thomson G, Walker J, Bennett A. A review of the risks and disease transmission associated with aerosol generating medical procedures. Journal of Infection Prevention. 2009;**10**(4):122-126. DOI: 10.1177/1757177409106456

[42] Després VR, Huffman JA, Burrows SM, Hoose C, Safatov AS, Buryak G, et al. Primary biological aerosol particles in the atmosphere: A review. Tellus B: Chemical and Physical Meteorology. 2012;**64**(1):2-20. DOI: 10.3402/tellusb.v64i0.15598

[43] Jones RM, Brosseau LM. Aerosol transmission of infectious disease. Journal of Occupational and Environmental Medicine. 2015;**57**(5):501-508. DOI: 10.1097/ JOM.0000000000000448

[44] Ijaz MK, Zargar B, Wright KE, Rubino JR, Sattar SA. Generic aspects of the airborne spread of human

pathogens indoors and emerging air decontamination technologies. American Journal of Infection Control. 2016;**44**(9 Suppl):S109-S120. DOI: 10.1016/j.ajic.2016.06.008

[45] Griffin DW, Kubilay N, Kocak M, Gray MA, Borden TC, Kellogg CA, et al. Airborne desert dust and aeromicrobiology over the Turkish Mediterranean coastline. Atmospheric Environment. 2007;**41**:4050-4062. DOI: 10.1016/j.atmosenv.2007.01.023

[46] Kellogg CA, Griffin DW, Garrison VH, Peak KK, Royall N, Smith RR, et al. Characterization of aerosolized bacteria and fungi from desert dust events in Mali, West Africa. Aerobiologia. 2004;**20**:99-110. DOI: 10.1023/B:AERO.0000032947.88335.bb

[47] Akerman M, Valentine-Maher S, Rao M, Taningco G, Khan P, Tuysugoglu G, et al. Allergen sensitivity and asthma severity at an inner city asthma center. The Journal of Asthma. 2003;**40**(1):55. DOI: 10.1081/JAS-120017207

[48] Shinn EA, Smith GW, Prospero JM, Betzer P, Hayes ML, Garrison V, et al. African dust and the demise of Caribbean coral reefs. Geophysical Research Letters. 2000;**27**(19):3029-3032. DOI: 10.1029/2000GL011599

[49] Weir-Brush JR, Garrison VH, Smith GW, Shinn EA. The relationship between gorgonian coral (Cnidaria: Gorganacea) diseases and African dust storms. Aerobiologia. 2004;**20**(2):119-126. DOI: 10.1023/B:AERO.0000032949. 14023.3a

[50] Wilken JA, Sondermeyer G, Shusterman D, McNary J, Vugia DJ, McDowell A, et al. Coccidioidomycosis among workers constructing solar power farms, California, USA, 2011- 2014. Emerging Infectious Diseases. 2015;**21**(11):1997-2005. DOI: 10.3201/ eid2111.150129

[51] Haig CW, Mackay WG, Walker JT, Williams C. Bioaerosol sampling mechanisms, bioefficiency and field studies. The Journal of Hospital Infection. 2016;**93**(3):242-255. DOI: 10.1016/j.jhin.2016.03.017

[52] Tang JW. The effect of environmental parameters on the survival of airborne infectious agents. Journal of the Royal Society Interfac. 2009;**6**(Suppl):S737-S746. DOI: DOI. 10.1098/rsif.2009.0227.focus

[53] Peccia J, Werth HM, Miller S, Hernandez. Effects of relative humidity on the ultraviolet induced inactivation of airborne bacteria. Aerosol Science and Technology. 2001;**35**(3):728-740. DOI: 10.1080/02786820152546770

[54] Won E, Ross H. Reaction of Airborne Rhizobium meliloti to some environmental factors. Applied Microbiology. 1969;**18**:556-557

[55] Tsai FC, Macher JM. Concentrations of airborne culturable bacteria in 100 large US office buildings from the BASE study. Indoor Air. 2005;**15**(Suppl 9):71-81. DOI: 10.1111/j.1600-0668.2005.00346

[56] Viegas C, Viegas S, Gomes A, Täubel M, Sabino R, editors. Exposure to Microbiological Agents in Indoor and Occupational Environments. 1st ed. Springer International Publishing AG; 2017. 415 p. DOI: 10.1007/978-3-319-61688-9

[57] Tang W, Kuehn TH, Simcik MF. Effects of temperature, humidity and air flow on fungal growth rate on loaded ventilation filters. Journal of Occupational and Environmental Hygiene. 2015;**12**(8):525-537. DOI: 10.1080/15459624.2015.1019076

[58] zur Nieden HA, Jankofsky M, Stilianakis NI, Boedeker R-H, Eikmann TF. Effects of bioaerosol polluted outdoor air on airways of residents:

**71**

*Long-Distance LIDAR Mapping Schematic for Fast Monitoring of Bioaerosol Pollution over Large…*

*DOI: http://dx.doi.org/10.5772/intechopen.87031*

A cross sectional study. Occupational and Environmental Medicine. 2003;**60**(5):336-342. DOI: 10.1136/

Aerobiology and the global transport of desert dust. Trends in Ecology & Evolution. 2006;**21**(11):638-644. DOI:

[59] Kellogg CA, Griffin DW.

10.1016/j.tree.2006.07.004

10.3390/atmos8120239

ijs.0.64029-0

et al. *Bacillus aerius* sp. nov., *Bacillus aerophilus* sp. nov., *Bacillus stratosphericus* sp. nov. and *Bacillus altitudinis* sp. nov., isolated from cryogenic tubes used for collecting air samples from high altitudes. International Journal of Systematic and Evolutionary Microbiology.

[60] Bragoszewska E, Mainka A, Pastuszka JS. Concentration and size distribution of culturable bacteria in ambient air during spring and winter in Gliwice: A typical urban area. Atmosphere. 2017;**8**(12):239-252. DOI:

[61] Shivaji S, Chaturvedi P, Suresh K, Reddy GS, Dutt CB, Wainwright M,

2006;**56**(Pt 7):1465-1473. DOI: 10.1099/

oem.60.5.336

*Long-Distance LIDAR Mapping Schematic for Fast Monitoring of Bioaerosol Pollution over Large… DOI: http://dx.doi.org/10.5772/intechopen.87031*

A cross sectional study. Occupational and Environmental Medicine. 2003;**60**(5):336-342. DOI: 10.1136/ oem.60.5.336

*Atmospheric Air Pollution and Monitoring*

pathogens indoors and emerging air decontamination technologies. American Journal of Infection Control. 2016;**44**(9 Suppl):S109-S120. DOI:

[51] Haig CW, Mackay WG, Walker JT, Williams C. Bioaerosol sampling mechanisms, bioefficiency and field studies. The Journal of Hospital Infection. 2016;**93**(3):242-255. DOI:

10.1016/j.jhin.2016.03.017

[52] Tang JW. The effect of environmental parameters on the survival of airborne infectious agents. Journal of the Royal Society Interfac. 2009;**6**(Suppl):S737-S746. DOI: DOI.

10.1098/rsif.2009.0227.focus

[53] Peccia J, Werth HM, Miller S, Hernandez. Effects of relative humidity on the ultraviolet induced inactivation of airborne bacteria. Aerosol Science and Technology. 2001;**35**(3):728-740. DOI: 10.1080/02786820152546770

[54] Won E, Ross H. Reaction of Airborne Rhizobium meliloti to some environmental factors. Applied Microbiology. 1969;**18**:556-557

of airborne culturable bacteria in 100 large US office buildings from the BASE study. Indoor Air. 2005;**15**(Suppl 9):71-81. DOI: 10.1111/j.1600-0668.2005.00346

[56] Viegas C, Viegas S, Gomes A, Täubel M, Sabino R, editors. Exposure to Microbiological Agents in Indoor and Occupational Environments. 1st ed. Springer International Publishing AG; 2017. 415 p. DOI: 10.1007/978-3-319-61688-9

[57] Tang W, Kuehn TH, Simcik MF. Effects of temperature, humidity and air flow on fungal growth rate on loaded ventilation filters. Journal of Occupational and Environmental Hygiene. 2015;**12**(8):525-537. DOI: 10.1080/15459624.2015.1019076

[58] zur Nieden HA, Jankofsky M, Stilianakis NI, Boedeker R-H, Eikmann TF. Effects of bioaerosol polluted outdoor air on airways of residents:

[55] Tsai FC, Macher JM. Concentrations

[45] Griffin DW, Kubilay N, Kocak M, Gray MA, Borden TC, Kellogg CA, et al. Airborne desert dust and aeromicrobiology over the Turkish Mediterranean coastline. Atmospheric Environment. 2007;**41**:4050-4062. DOI:

10.1016/j.atmosenv.2007.01.023

[46] Kellogg CA, Griffin DW, Garrison VH, Peak KK, Royall N, Smith RR, et al. Characterization of aerosolized bacteria and fungi from desert dust events in Mali, West Africa. Aerobiologia. 2004;**20**:99-110. DOI: 10.1023/B:AERO.0000032947.88335.bb

[47] Akerman M, Valentine-Maher S, Rao M, Taningco G, Khan P, Tuysugoglu G, et al. Allergen sensitivity and asthma severity at an inner city asthma center. The Journal of Asthma. 2003;**40**(1):55.

[48] Shinn EA, Smith GW, Prospero JM, Betzer P, Hayes ML, Garrison V, et al. African dust and the demise of Caribbean coral reefs. Geophysical Research Letters. 2000;**27**(19):3029-3032. DOI: 10.1029/2000GL011599

[49] Weir-Brush JR, Garrison VH, Smith GW, Shinn EA. The relationship between gorgonian coral (Cnidaria: Gorganacea) diseases and African dust storms. Aerobiologia. 2004;**20**(2):119-126. DOI: 10.1023/B:AERO.0000032949.

[50] Wilken JA, Sondermeyer G, Shusterman D, McNary J, Vugia DJ, McDowell A, et al. Coccidioidomycosis among workers constructing solar power farms, California, USA, 2011- 2014. Emerging Infectious Diseases. 2015;**21**(11):1997-2005. DOI: 10.3201/

DOI: 10.1081/JAS-120017207

10.1016/j.ajic.2016.06.008

**70**

eid2111.150129

14023.3a

[59] Kellogg CA, Griffin DW. Aerobiology and the global transport of desert dust. Trends in Ecology & Evolution. 2006;**21**(11):638-644. DOI: 10.1016/j.tree.2006.07.004

[60] Bragoszewska E, Mainka A, Pastuszka JS. Concentration and size distribution of culturable bacteria in ambient air during spring and winter in Gliwice: A typical urban area. Atmosphere. 2017;**8**(12):239-252. DOI: 10.3390/atmos8120239

[61] Shivaji S, Chaturvedi P, Suresh K, Reddy GS, Dutt CB, Wainwright M, et al. *Bacillus aerius* sp. nov., *Bacillus aerophilus* sp. nov., *Bacillus stratosphericus* sp. nov. and *Bacillus altitudinis* sp. nov., isolated from cryogenic tubes used for collecting air samples from high altitudes. International Journal of Systematic and Evolutionary Microbiology. 2006;**56**(Pt 7):1465-1473. DOI: 10.1099/ ijs.0.64029-0

**73**

**Chapter 6**

**Abstract**

also predicted in this chapter.

**1. Introduction**

Smart Environment Monitoring

Network: A Comparative Study

*Tabbsum Mujawar and Lalasaheb Deshmukh*

System Using Wired and Wireless

This chapter focuses on the implementation of a smart environment monitoring system using wired and wireless sensor networks (WSN). The goal was to develop a LabVIEW based system to monitor environmental parameters that provide inaccessible, real-time monitoring. The development of portable and efficient environment monitoring system based on LabVIEW GUI that monitors various environmental parameters such as temperature, relative humidity, Air quality and light intensity was developed. This chapter targets on both wired and wireless approach for environment monitoring. The limitations of wired network were explained by flourishing the portable system. For proceedings with the impediment and insufficiency of wired network, Arduino augmentation ascendancy, are mingled with XBee wireless sensor network. The data from the environment was sent to the sink node wirelessly through mote. Monitoring of the data was done in a personal computer (PC) through a graphical user interface made by LabVIEW. The pertinent sensor for each was connected to analog input of Arduino UNO and their values are displayed on front panel of LabVIEW. LabVIEW run time engine makes the system cost effective and facile. To reveal the effectiveness of the system, some measurement results are

**Keywords:** wired network, wireless sensor network, LabVIEW, web publishing tool

Environment monitoring plays an important role in all the sectors. It is a forthcoming relevance field which is of fastidious rate to our country. Metropolitan cities with superior absorption of industry, rigorous transportation and soaring population mass are major sources of air pollution, which results in monitoring of environment. To think about the environment, it has turned out to be one of the prime concerns for almost every country in the world. Due to enormous increased in industrialization, the recent condition is obviously altering towards more environment gracious solutions. This chapter discusses the different environmental and air quality parameters using respective sensors for it and provides various opportune services for users who can administer the information via a website from long-distance. This system comprises of both wired and wireless networks. Wired communications is a wide name used to portray the communication process that utilizes the cables and wiring to convey the data. Usually, wired communications are appreciated widely by research community due to its stability in services. They are not influenced

## **Chapter 6**

## Smart Environment Monitoring System Using Wired and Wireless Network: A Comparative Study

*Tabbsum Mujawar and Lalasaheb Deshmukh*

## **Abstract**

This chapter focuses on the implementation of a smart environment monitoring system using wired and wireless sensor networks (WSN). The goal was to develop a LabVIEW based system to monitor environmental parameters that provide inaccessible, real-time monitoring. The development of portable and efficient environment monitoring system based on LabVIEW GUI that monitors various environmental parameters such as temperature, relative humidity, Air quality and light intensity was developed. This chapter targets on both wired and wireless approach for environment monitoring. The limitations of wired network were explained by flourishing the portable system. For proceedings with the impediment and insufficiency of wired network, Arduino augmentation ascendancy, are mingled with XBee wireless sensor network. The data from the environment was sent to the sink node wirelessly through mote. Monitoring of the data was done in a personal computer (PC) through a graphical user interface made by LabVIEW. The pertinent sensor for each was connected to analog input of Arduino UNO and their values are displayed on front panel of LabVIEW. LabVIEW run time engine makes the system cost effective and facile. To reveal the effectiveness of the system, some measurement results are also predicted in this chapter.

**Keywords:** wired network, wireless sensor network, LabVIEW, web publishing tool

## **1. Introduction**

Environment monitoring plays an important role in all the sectors. It is a forthcoming relevance field which is of fastidious rate to our country. Metropolitan cities with superior absorption of industry, rigorous transportation and soaring population mass are major sources of air pollution, which results in monitoring of environment. To think about the environment, it has turned out to be one of the prime concerns for almost every country in the world. Due to enormous increased in industrialization, the recent condition is obviously altering towards more environment gracious solutions.

This chapter discusses the different environmental and air quality parameters using respective sensors for it and provides various opportune services for users who can administer the information via a website from long-distance. This system comprises of both wired and wireless networks. Wired communications is a wide name used to portray the communication process that utilizes the cables and wiring to convey the data. Usually, wired communications are appreciated widely by research community due to its stability in services. They are not influenced

by external environmental effects as compared to wireless networks. For some services, the potency and pace of the communication is finer to other solutions, such as satellite. Due to this distinctiveness, wired connections linger trendy, yet wireless system sustained to proceed. Environment monitoring with wired network have some limitations such that wired sensors could not be implemented in remote areas. Also it is very complex and costly to mount and sustain the wired networks [1]. Additionally, if a wire between the two devices gets breaks, the communication between these two gets collapsed; hence, the entire network will also fail.

Letter the initiative of replacing the wired with wireless network was brought and it overcomes approximately all the troubles with the wired communication nevertheless it hold disadvantages of the sluggish bandwidth and growth of interference. Wireless communications is a rapidly increasing technology endow with the litheness and mobility in our environment. The noticeable benefit of wireless transmission is a key diminution and simplification in wiring. The cabling cost in industrial installations is 130–650 US\$ per meter and using wireless technology, it would be eradicated around 20–80% [1]. The skillful organization of the equipment through efficient monitoring of the environment augments an additional hoard in terms of cost, e.g., Wang et al. [2]. The wireless system developed by Honeywell to scrutinize steam traps saves the total cost effectively about 100,000–300,000 US\$ annually [2]. The impracticable sensor applications' technology, viz. monitoring far-off areas and locations, is featured with unrestricted mechanism and litheness for sensors and augmented the network heftiness. Moreover, WSN technology makes the system reliable and less costly. It allows more rapidly exploitation and deployment of different sensors because this network provides various properties to the sensor nodes. Further, an integration of WSN technology with MEMS makes the motes with enormously stumpy cost, miniature sized and least power. MEMS are the inertial sensors, pressure sensors, temperature sensors, humidity sensors, strain-gage sensors and various piezo and capacitive sensors for proximity. Over the last decade, the technology of wireless sensor network (WSN) has been widely used in many real time applications and these miniaturized sensors can sense, process and communicate. Most wireless sensor nodes are capable of measuring temperature, acceleration, light, illumination, humidity; level of gases and chemical materials in the surrounding environment.

WSN is a compilation of wireless sensor nodes. A WSN is also an amalgamation of an integer of motes with limited communication ability. The co-ordination between the sensor nodes provides ability to process and to gather information in a large amount [3, 4]. Also, ad-hoc networks can be created. Generally, WSN networks are categorized in two types: structured and unstructured. In unstructured WSN, the sensor nodes are deployed in an ad-hoc manner without any careful planning. Once nodes are deployed, monitoring and processing of data is done in unattended environment. In structured WSN, motes are deployed in preplanned approach. The structured wireless sensor network is superior to unstructured one, because cost and maintenance required to deploy the node are less. The nodes in structured WSN are positioned at exact locations to offer coverage, whereas unstructured deployment has uncovered areas. Wireless sensor network aims to give co-ordination among the physical conditions and the internet globe. It has the following features:

**75**

**2. Literature survey**

*Smart Environment Monitoring System Using Wired and Wireless Network: A Comparative Study*

Tilak et al. [5] have shown that the intellectual sensors can gather data from disaster area, floods and also from revolutionary attacks. The network is promising for

Due to the above compensation WSN becomes vital part of near future applications. By using WSN based environmental monitoring system it is possible to transform the customarily environmental monitoring methods. Conventionally data loggers were used to collect the data from environment and this was time overriding and fairly costly. To avoid the drawbacks of this system, we developed a system which is portable and cost effective. The LabVIEW (Laboratory Virtual Instrument Engineering Workbench) and an Arduino IDE are the programming tools used for this system. But, the writing programming is mostly used in Arduino [6]. Meanwhile, LabVIEW uses a programming type of block diagram. In the present system, it is decided to use the Arduino platform or microcontroller for the deployment of WSN nodes. This is an embedded board having included USB competence. The miniature and user responsive nature makes it more superior than other advanced microcontrollers. These microcontrollers have more on chip facilities such as +5 V, analog and digital pins. It does not have on board power jack. Due to the auto switching capability of ATMega 328 microcontroller, no external power jumpers are required. The use of an Arduino simplifies the process of working with microcontrollers and additionally it offers some advantages to the users over other systems such as cross-platform, simple, clear programming environment, open source and extensible software and hardware. Arduino platform has good specifications e.g. cheap, easy to use and wide varieties of shields that have been emerged with many different purposes such as Ethernet and GSM support. While, if we want to create a multifunction code for carrying out on multicore processors this would be possible using LabVIEW tool. It has a graphical palette to create and run VI's. Any complex programming can be done easily using the tools available in this software. This environment monitoring system uses the web publishing tool to display the monitored data on the web page for remote monitoring. In this chapter,

*DOI: http://dx.doi.org/10.5772/intechopen.86316*

• Cost effective

• Easy to install.

• Collection of information

• Dealing out of information easily and

• Environment monitoring for numerous applications.

we studied both wired and wireless environment monitoring system.

To endeavor with the environment monitoring, momentous accomplishment was born out in shrewd and diminish this technique. The manifestation of environment monitoring and WSN related facts are premeditated by a lot of investigator and have proclaimed demographic data incidents. In 2008, Yang et al. [7] disclosed,

Sensors" fixate a novel environmental monitoring system with a concentrate on the comprehensive system planning for smooth alliance of wired and wireless sensors for long-term, inaccessible monitoring. A consolidated plan for sensor data assem-

"An Environmental Monitoring System with Integrated Wired and Wireless

bly, execution, promulgation was also presented in this paper.


*Smart Environment Monitoring System Using Wired and Wireless Network: A Comparative Study DOI: http://dx.doi.org/10.5772/intechopen.86316*

• Cost effective

*Atmospheric Air Pollution and Monitoring*

cal materials in the surrounding environment.

by external environmental effects as compared to wireless networks. For some services, the potency and pace of the communication is finer to other solutions, such as satellite. Due to this distinctiveness, wired connections linger trendy, yet wireless system sustained to proceed. Environment monitoring with wired network have some limitations such that wired sensors could not be implemented in remote areas. Also it is very complex and costly to mount and sustain the wired networks [1]. Additionally, if a wire between the two devices gets breaks, the communication

between these two gets collapsed; hence, the entire network will also fail.

Letter the initiative of replacing the wired with wireless network was brought and it overcomes approximately all the troubles with the wired communication nevertheless it hold disadvantages of the sluggish bandwidth and growth of interference. Wireless communications is a rapidly increasing technology endow with the litheness and mobility in our environment. The noticeable benefit of wireless transmission is a key diminution and simplification in wiring. The cabling cost in industrial installations is 130–650 US\$ per meter and using wireless technology, it would be eradicated around 20–80% [1]. The skillful organization of the equipment through efficient monitoring of the environment augments an additional hoard in terms of cost, e.g., Wang et al. [2]. The wireless system developed by Honeywell to scrutinize steam traps saves the total cost effectively about 100,000–300,000 US\$ annually [2]. The impracticable sensor applications' technology, viz. monitoring far-off areas and locations, is featured with unrestricted mechanism and litheness for sensors and augmented the network heftiness. Moreover, WSN technology makes the system reliable and less costly. It allows more rapidly exploitation and deployment of different sensors because this network provides various properties to the sensor nodes. Further, an integration of WSN technology with MEMS makes the motes with enormously stumpy cost, miniature sized and least power. MEMS are the inertial sensors, pressure sensors, temperature sensors, humidity sensors, strain-gage sensors and various piezo and capacitive sensors for proximity. Over the last decade, the technology of wireless sensor network (WSN) has been widely used in many real time applications and these miniaturized sensors can sense, process and communicate. Most wireless sensor nodes are capable of measuring temperature, acceleration, light, illumination, humidity; level of gases and chemi-

WSN is a compilation of wireless sensor nodes. A WSN is also an amalgamation of an integer of motes with limited communication ability. The co-ordination between the sensor nodes provides ability to process and to gather information in a large amount [3, 4]. Also, ad-hoc networks can be created. Generally, WSN networks are categorized in two types: structured and unstructured. In unstructured WSN, the sensor nodes are deployed in an ad-hoc manner without any careful planning. Once nodes are deployed, monitoring and processing of data is done in unattended environment. In structured WSN, motes are deployed in preplanned approach. The structured wireless sensor network is superior to unstructured one, because cost and maintenance required to deploy the node are less. The nodes in structured WSN are positioned at exact locations to offer coverage, whereas unstructured deployment has uncovered areas. Wireless sensor network aims to give co-ordination among the physical conditions and the internet globe. It has the

**74**

following features:

• More accurate

• Flexible in nature

• WSN should be reliable

• Easy to install.

Tilak et al. [5] have shown that the intellectual sensors can gather data from disaster area, floods and also from revolutionary attacks. The network is promising for


Due to the above compensation WSN becomes vital part of near future applications. By using WSN based environmental monitoring system it is possible to transform the customarily environmental monitoring methods. Conventionally data loggers were used to collect the data from environment and this was time overriding and fairly costly. To avoid the drawbacks of this system, we developed a system which is portable and cost effective. The LabVIEW (Laboratory Virtual Instrument Engineering Workbench) and an Arduino IDE are the programming tools used for this system. But, the writing programming is mostly used in Arduino [6]. Meanwhile, LabVIEW uses a programming type of block diagram. In the present system, it is decided to use the Arduino platform or microcontroller for the deployment of WSN nodes. This is an embedded board having included USB competence. The miniature and user responsive nature makes it more superior than other advanced microcontrollers. These microcontrollers have more on chip facilities such as +5 V, analog and digital pins. It does not have on board power jack. Due to the auto switching capability of ATMega 328 microcontroller, no external power jumpers are required. The use of an Arduino simplifies the process of working with microcontrollers and additionally it offers some advantages to the users over other systems such as cross-platform, simple, clear programming environment, open source and extensible software and hardware. Arduino platform has good specifications e.g. cheap, easy to use and wide varieties of shields that have been emerged with many different purposes such as Ethernet and GSM support. While, if we want to create a multifunction code for carrying out on multicore processors this would be possible using LabVIEW tool. It has a graphical palette to create and run VI's. Any complex programming can be done easily using the tools available in this software. This environment monitoring system uses the web publishing tool to display the monitored data on the web page for remote monitoring. In this chapter, we studied both wired and wireless environment monitoring system.

### **2. Literature survey**

To endeavor with the environment monitoring, momentous accomplishment was born out in shrewd and diminish this technique. The manifestation of environment monitoring and WSN related facts are premeditated by a lot of investigator and have proclaimed demographic data incidents. In 2008, Yang et al. [7] disclosed, "An Environmental Monitoring System with Integrated Wired and Wireless Sensors" fixate a novel environmental monitoring system with a concentrate on the comprehensive system planning for smooth alliance of wired and wireless sensors for long-term, inaccessible monitoring. A consolidated plan for sensor data assembly, execution, promulgation was also presented in this paper.

In 2009, Flammini et al. [8] reported, "Wired and wireless sensor networks for industrial applications" noted a real-time sensor networks for industrial applications. Particular consideration has been compensated to the explanation of arrangements and avenue for completion evaluation was conferred. This paper represents the limitations of wired network and how it is overcome by wireless sensor network.

Kaur et al. [9], in 2014, narrated, "Comparisons of wired and wireless networks: A review", revealed the resemblance between wired and wireless networking on the basis of disparate hardware demand, ranges, flexibility, accuracy and assets. Wired and Wireless networks are more trivial in the private sectors as well as in the household applications. The wired networks administer a defended and swift connectivity but the need of movability, i.e., in any place, anytime is sway the network users close to wireless technology.

## **3. Wired communication technology**

The general block diagram of wired environment monitoring system is shown in **Figure 1**.

This system helps to make the cities pollution free. It monitors the contaminant air and informs about the level of pollution in the air. The wired approach of the system consists of SY-HS-220 humidity sensor, MQ-135 air quality sensor, LDR, LM-35 temperature sensor etc. These sensors output are connected at analog inputs of an Arduino microcontroller. All these sensors are placed over the area to detect the different levels of pollution. The system also makes use of Arduino module, LCD, buzzer and LED's. The LCD screen is used to display the level of pollution within Solapur University campus. It exhibits the category of pollution level. The system puts on buzzer when the level of pollution crosses its threshold limit. Thus this system helps to keep the Solapur University campus pollution free by informing about pollution levels of the areas. This system cost effective and portable. The circuit set up for wired system and its connections are shown in **Figure 2**. The system using wired network is portable and effective. But, drawback of such a system is that cables requirement for providing linkage between the devices. As number of

**77**

**Figure 2.**

**Figure 3.**

**Figure 4.**

*Circuit connection of a system.*

**4. Wireless communication technology**

*Actual readings of each sensor on LCD.*

*Experimental set up for wired environment monitoring system.*

Due to advancement in technology wireless network being used to avoid cabling cost and to obtain efficient control. We proposed to use WSN technology for it. In today's world, the wireless sensor networks (WSN) is one of the most momentous technology. The monitoring, reorganization and controlling of the data are the key

*Smart Environment Monitoring System Using Wired and Wireless Network: A Comparative Study*

peripheral increased in the system, it leads to lofty installation and protection costs, e.g., due to low scalability and more breakdown rate of connectors. Consequently,

wireless technology is the best solution for todays (**Figures 3** and **4**).

*DOI: http://dx.doi.org/10.5772/intechopen.86316*

**Figure 1.** *General block diagram of wired system.*

*Smart Environment Monitoring System Using Wired and Wireless Network: A Comparative Study DOI: http://dx.doi.org/10.5772/intechopen.86316*

peripheral increased in the system, it leads to lofty installation and protection costs, e.g., due to low scalability and more breakdown rate of connectors. Consequently, wireless technology is the best solution for todays (**Figures 3** and **4**).

**Figure 2.** *Circuit connection of a system.*

*Atmospheric Air Pollution and Monitoring*

users close to wireless technology.

in **Figure 1**.

**3. Wired communication technology**

In 2009, Flammini et al. [8] reported, "Wired and wireless sensor networks for industrial applications" noted a real-time sensor networks for industrial applications. Particular consideration has been compensated to the explanation of arrangements and avenue for completion evaluation was conferred. This paper represents the limitations of wired network and how it is overcome by wireless sensor network. Kaur et al. [9], in 2014, narrated, "Comparisons of wired and wireless networks: A review", revealed the resemblance between wired and wireless networking on the basis of disparate hardware demand, ranges, flexibility, accuracy and assets. Wired and Wireless networks are more trivial in the private sectors as well as in the household applications. The wired networks administer a defended and swift connectivity but the need of movability, i.e., in any place, anytime is sway the network

The general block diagram of wired environment monitoring system is shown

This system helps to make the cities pollution free. It monitors the contaminant air and informs about the level of pollution in the air. The wired approach of the system consists of SY-HS-220 humidity sensor, MQ-135 air quality sensor, LDR, LM-35 temperature sensor etc. These sensors output are connected at analog inputs of an Arduino microcontroller. All these sensors are placed over the area to detect the different levels of pollution. The system also makes use of Arduino module, LCD, buzzer and LED's. The LCD screen is used to display the level of pollution within Solapur University campus. It exhibits the category of pollution level. The system puts on buzzer when the level of pollution crosses its threshold limit. Thus this system helps to keep the Solapur University campus pollution free by informing about pollution levels of the areas. This system cost effective and portable. The circuit set up for wired system and its connections are shown in **Figure 2**. The system using wired network is portable and effective. But, drawback of such a system is that cables requirement for providing linkage between the devices. As number of

**76**

**Figure 1.**

*General block diagram of wired system.*

#### **Figure 3.** *Experimental set up for wired environment monitoring system.*

**Figure 4.** *Actual readings of each sensor on LCD.*

## **4. Wireless communication technology**

Due to advancement in technology wireless network being used to avoid cabling cost and to obtain efficient control. We proposed to use WSN technology for it. In today's world, the wireless sensor networks (WSN) is one of the most momentous technology. The monitoring, reorganization and controlling of the data are the key

concern of this technology. The inaccessible interface and actual monitoring with the physical world can be done easily by sensor node of the network. The wireless sensor networks differ from general data networks, because WSN are application oriented, planned and deployed for dedicated purpose [10].

The whole system was designed using ATMega 328 microcontroller integrated with XBee S2 protocol to form sensing phenomena. The planning of mote consists of a processing entity conscientious for compilation and giving out the data sensed by a sensor. A radio transceiver mechanism used as a communication part accompanied by the sensors and a battery is the power provide unit in this system. We anticipated four sensors for measuring temperature, humidity, air quality and light intensity within Solapur University campus. The humidity SY-HS 220 sensor module is used for measuring humidity. Its operating voltage and temperature is 5 V, 0–60°C. The −30 to 85°C is storage temperature range of this module. It converts relative humidity to voltage and can be used in environment monitoring applications. LDR sensor and its voltage divider circuit were used to measured light intensity. LM 35 temperature sensor gives 10 mV per 1° rise in temperature. While, MQ-135 performs ambient air quality monitoring. The circuit connection of sensors to Arduino is shown in **Figure 2**. The developed WSN system uses two motes and one sink node.

The general block diagram of wireless environment monitoring system is shown in **Figure 5**.

According to **Figure 5**, working of the proposed system is carried out. Initially, we have calibrated the individual sensors and then connected to analog inputs of an Arduino microcontroller. This controller integrated with 10-bit ADC, which renovate the analog signals into digital output (**Figures 6** and **7**).

The results are displayed on the serial monitor window of Arduino. Through VISA function tool, it is displayed on the front panel of the LabVIEW. For this, we used LabVIEW run time engine, which means that without installation of LabVIEW on your computer, you can run any LabVIEW program, which reduces cost of the system. The developed GUI (Graphical User Interface) using LabVIEW for system continuously monitors the environment data [11]. In X-CTU software, individual ID's for each motes are specified [12–14] (**Tables 1** and **2**).

**79**

**Figure 6.**

**Figure 7.**

*Circuit connection of sink node.*

*Circuit connection of sensor node.*

*Smart Environment Monitoring System Using Wired and Wireless Network: A Comparative Study*

The data from each mote was separated using LabVIEW software as follows:

• Sink node is a common receiver, which receives data from several motes,

The LabVIEW GUI was used to monitor the environment quality level. LabVIEW is scheme-intend software that allows to program tools on a GUI for the

These tools are very interactive and superficial for encoding. We can amend the

(Virtual Instrument Software Architecture),

• After this division of the data was carried out.

measurement and control of the systems.

• The sink node (Arduino UNO board) was interfaced to LabVIEW through VISA

LabVIEW is a graphical improvement tool developed by a National Instruments.

*DOI: http://dx.doi.org/10.5772/intechopen.86316*

**Figure 5.** *General block diagram of wireless system.*

*Smart Environment Monitoring System Using Wired and Wireless Network: A Comparative Study DOI: http://dx.doi.org/10.5772/intechopen.86316*

## **Figure 6.**

*Atmospheric Air Pollution and Monitoring*

one sink node.

in **Figure 5**.

concern of this technology. The inaccessible interface and actual monitoring with the physical world can be done easily by sensor node of the network. The wireless sensor networks differ from general data networks, because WSN are application

The whole system was designed using ATMega 328 microcontroller integrated with XBee S2 protocol to form sensing phenomena. The planning of mote consists of a processing entity conscientious for compilation and giving out the data sensed by a sensor. A radio transceiver mechanism used as a communication part accompanied by the sensors and a battery is the power provide unit in this system. We anticipated four sensors for measuring temperature, humidity, air quality and light intensity within Solapur University campus. The humidity SY-HS 220 sensor module is used for measuring humidity. Its operating voltage and temperature is 5 V, 0–60°C. The −30 to 85°C is storage temperature range of this module. It converts relative humidity to voltage and can be used in environment monitoring applications. LDR sensor and its voltage divider circuit were used to measured light intensity. LM 35 temperature sensor gives 10 mV per 1° rise in temperature. While, MQ-135 performs ambient air quality monitoring. The circuit connection of sensors to Arduino is shown in **Figure 2**. The developed WSN system uses two motes and

The general block diagram of wireless environment monitoring system is shown

According to **Figure 5**, working of the proposed system is carried out. Initially, we have calibrated the individual sensors and then connected to analog inputs of an Arduino microcontroller. This controller integrated with 10-bit ADC, which

The results are displayed on the serial monitor window of Arduino. Through VISA function tool, it is displayed on the front panel of the LabVIEW. For this, we used LabVIEW run time engine, which means that without installation of LabVIEW on your computer, you can run any LabVIEW program, which reduces cost of the system. The developed GUI (Graphical User Interface) using LabVIEW for system continuously monitors the environment data [11]. In X-CTU software,

renovate the analog signals into digital output (**Figures 6** and **7**).

individual ID's for each motes are specified [12–14] (**Tables 1** and **2**).

oriented, planned and deployed for dedicated purpose [10].

**78**

**Figure 5.**

*General block diagram of wireless system.*

*Circuit connection of sensor node.*

**Figure 7.**

*Circuit connection of sink node.*

The data from each mote was separated using LabVIEW software as follows:


The LabVIEW GUI was used to monitor the environment quality level. LabVIEW is scheme-intend software that allows to program tools on a GUI for the measurement and control of the systems.

LabVIEW is a graphical improvement tool developed by a National Instruments. These tools are very interactive and superficial for encoding. We can amend the


#### **Table 1.**

*XBee parameter for router.*


**81**

**Figure 8.**

*Graphical representation of sensor node 1.*

*Smart Environment Monitoring System Using Wired and Wireless Network: A Comparative Study*

**Sr. no. Parameters name Parameters symbol Configuration value**

17. DI06 configuration DI06 0 18. IO sampling rate IR 3E8 19. Parity NB 0 20. RSSI of last packet DB 0

indoctrination gush as we want. The proficient machine code is the distinctive chattels of LabVIEW. The developed G-code of LabVIEW is more indulgent and required execution time is less. The freeware driver makes it more intuitive. The communication, instrumentation, neural networking, control system etc. tools in the LabVIEW have its own task to engender G-code relating to this. The current wireless system uses web publishing tool to show the monitored information on the

When sensor nodes are placed within Solapur University campus, it continuously monitors an environment, the readings from each sensor node will send to the gateway node. This gateway node will send the data to LabVIEW through VISA. The graphical representation of sensor node 1 and sensor node 2 are depicted

**Figure 10** represents the GUI crated by LabVIEW for environment monitoring

The LabVIEW programming for this was done as follows (**Figure 11**). For real time monitoring of environment system, we used a web publishing tool in LabVIEW. This tool was used for web portal connectivity to cover stout

By accessing the web server, inaccessible monitoring and controlling of this system was done using a web publishing tool. Based on the parameters

*DOI: http://dx.doi.org/10.5772/intechopen.86316*

web page for distant monitoring.

*XBee parameter for coordinator.*

**5. Results**

**Table 2.**

system.

in the **Figures 8** and **9**.

monitoring vicinity.

*Smart Environment Monitoring System Using Wired and Wireless Network: A Comparative Study DOI: http://dx.doi.org/10.5772/intechopen.86316*


#### **Table 2.**

*Atmospheric Air Pollution and Monitoring*

**Sr. no. Parameters name Parameters symbol Configuration value**

**Sr. no. Parameters name Parameters symbol Configuration value**

1. PAN ID ID 100 2. Destination address high DH 0 3. Destination address low DL 0 4. Scan channel SC FFFF(Hex Value) 5. Scan duration SD 3 6. Channel verifications JV 1 7. Device option D0 1 8. Node identifier NI Node 1 to Node 2 9. Node join time NJ FF(Hex Value) 10. Node discovery back off NT 3C 11. Power level PL 4 12. Power mode PM 1 13. Power at PL4 PP 3 14. Baud rate BD 3 15. RSSI PWM timer RP 28 16. DI07 configuration DI07 1 17. DI06 configuration DI06 0 18. IO sampling rate IR 3E8 19. Parity NB 0 20. RSSI of last packet DB 0

1. PAN ID ID 100 2. Destination address high DH 0 3. Destination address low DL 0 4. Scan channel SC FFFF(Hex Value) 5. Scan duration SD 3 6. Channel verifications JV 0 7. Device option D0 1 8. Node identifier NI — 9. Node join time NJ FF 10. Node discovery back off NT 3C 11. Power level PL 4 12. Power mode PM 1 13. Power at PL4 PP 3 14. Baud rate BD 3 15. RSSI PWM timer RP 28 16. DI07 configuration DI07 1

**80**

**Table 1.**

*XBee parameter for router.*

*XBee parameter for coordinator.*

indoctrination gush as we want. The proficient machine code is the distinctive chattels of LabVIEW. The developed G-code of LabVIEW is more indulgent and required execution time is less. The freeware driver makes it more intuitive. The communication, instrumentation, neural networking, control system etc. tools in the LabVIEW have its own task to engender G-code relating to this. The current wireless system uses web publishing tool to show the monitored information on the web page for distant monitoring.

## **5. Results**

When sensor nodes are placed within Solapur University campus, it continuously monitors an environment, the readings from each sensor node will send to the gateway node. This gateway node will send the data to LabVIEW through VISA. The graphical representation of sensor node 1 and sensor node 2 are depicted in the **Figures 8** and **9**.

**Figure 10** represents the GUI crated by LabVIEW for environment monitoring system.

The LabVIEW programming for this was done as follows (**Figure 11**).

For real time monitoring of environment system, we used a web publishing tool in LabVIEW. This tool was used for web portal connectivity to cover stout monitoring vicinity.

By accessing the web server, inaccessible monitoring and controlling of this system was done using a web publishing tool. Based on the parameters

*Graphical representation of sensor node 1.*

#### **Figure 9.**

*Graphical representation of sensor node 2.*

#### **Figure 10.**

*GUI of monitoring environmental parameters of sensor nodes.*

**83**

**Figure 13.**

**Figure 12.**

*The GUI of a system displayed on the web server before putting control over VI.*

*Distant monitoring of a system after putting control over VI.*

*Smart Environment Monitoring System Using Wired and Wireless Network: A Comparative Study*

specified in the program, this tool converts the front panel into HTML web page. **Figure 13** represents the GUI of the web server. Concisely, we built an effortless VI that monitors the wireless system. This application was launched on the internet and monitored it tenuously and controlled it involuntarily. For initiation this application on the internet, we must have to arrange the web access. Port address of LabVIEW is 8000. By enabling the various setting in a web publishing tool, we have a right of entry to access other unapproachable panel server and all other IP addresses which we desire. The URL obtained from the LabVIEW page is http://dell-pc:8000/intecopen.html. **Figure 12** shows the data monitoring system in the internet browser before putting a control over VI. When we put a control over VI, it is shown in **Figure 13**. The internet browsing of a system helps to monitor an environment quality at remote places continuously to the users. It

*DOI: http://dx.doi.org/10.5772/intechopen.86316*

is shown in **Figure 14**.

**Figure 11.** *The G-code for sensor nodes for environment monitoring.* *Smart Environment Monitoring System Using Wired and Wireless Network: A Comparative Study DOI: http://dx.doi.org/10.5772/intechopen.86316*

specified in the program, this tool converts the front panel into HTML web page. **Figure 13** represents the GUI of the web server. Concisely, we built an effortless VI that monitors the wireless system. This application was launched on the internet and monitored it tenuously and controlled it involuntarily. For initiation this application on the internet, we must have to arrange the web access. Port address of LabVIEW is 8000. By enabling the various setting in a web publishing tool, we have a right of entry to access other unapproachable panel server and all other IP addresses which we desire. The URL obtained from the LabVIEW page is http://dell-pc:8000/intecopen.html. **Figure 12** shows the data monitoring system in the internet browser before putting a control over VI. When we put a control over VI, it is shown in **Figure 13**. The internet browsing of a system helps to monitor an environment quality at remote places continuously to the users. It is shown in **Figure 14**.

#### **Figure 12.**

*Atmospheric Air Pollution and Monitoring*

*Graphical representation of sensor node 2.*

**82**

**Figure 11.**

**Figure 10.**

**Figure 9.**

*GUI of monitoring environmental parameters of sensor nodes.*

*The G-code for sensor nodes for environment monitoring.*

*The GUI of a system displayed on the web server before putting control over VI.*


**Figure 13.**

*Distant monitoring of a system after putting control over VI.*

**Figure 14.** *Front panel showing remote monitoring of a system.*

## **6. Conclusions**

A lucrative environmental monitoring method with least amount of components has been constructed. The system is successfully developed using wired and wireless networks. The limitations of wired network and opportunities using wireless networks are rigorously described. The environment monitoring sensors with an Atmega 368 microcontroller, Web portal is proposed. For sending and receiving of the data, the web publishing tool in LabVIEW is used. The system is developed using two motes and one sink node. XBee protocol is used to provide wireless access. This system provides a real-time monitoring via money-spinning low data rate and significant power wireless communication technology. We envisage that this system will encompass an enormous recognition in the industrialized sectors and will realize an effective amalgamation among WSN and Web portal. Accordingly, a tack target of inaccessible monitoring of the air quality within the environment can be attained. It is highly pertinent to metrological departments and also in industrial sectors. In future, we would be fond of to be made controlling system for environment monitoring.

## **Author details**

Tabbsum Mujawar\* and Lalasaheb Deshmukh School of Physical Sciences, Solapur University, Solapur, Maharashtra, India

\*Address all correspondence to: thmujawar@sus.ac.in

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**85**

*Smart Environment Monitoring System Using Wired and Wireless Network: A Comparative Study*

[10] Mujawar T, Kasbe M, Mule S, Deshmukh L. Development of wireless gas sensing system for home safety. International Journal of Engineering Sciences & Emerging Technologies.

[11] Mujawar T, Kasbe M, Mule S, Deshmukh L. Online monitoring of WSN based air quality monitoring system. AIP Conference Proceedings. 2018;**02003**:020030-1-020030-9

[12] Schell M, Guvench M. Development of a general purpose XBee series-2 API-mode communication library for LabVIEW. In: Northeast Section Conference (ASEE); April 27-28, 2012

[13] Zhang J, Song G. Design of a wireless sensor network based monitoring system for home automation. In: International Conference on Future Computer Sciences and Application.

2016;**8**:213-221

June 2011;**7**:18-19

[14] Li Y, Ji M. Design of home automation system based on ZigBee wireless sensor network. In: 1st International Conference on Information Science and Engineering (ICISE); December 26-28, 2009

*DOI: http://dx.doi.org/10.5772/intechopen.86316*

[1] Mujawar T. Development of wireless sensor network for hazardous gas detection and alert system [thesis]. Inflibnet. 30 Jan 2017. Available from: http://www.shodhgang.inflibnet.ac.in

[2] Wang N, Zhang N, Wang M. Wireless sensors in agriculture and food industry.

perspective. Computers and Electronics

[3] Suryadevara N, Mukhopadhyay S. Wireless sensor network based home monitoring system for wellness determination of elderly. IEEE Sensors

[4] Tiantian J, Zhanyong Y. Research on mine safety monitoring system based on WSN. Procedia Engineering.

[5] Tilak S, Ghazaleh N, Heinzelman W.

SIGMOBILE Mobile Computing and Communications Review. 2002;**6**:28-36

[6] Margolis M. Arduino Cookbook. 1st ed. Massachusetts, United States: O'REILLY® Media, Inc.; 2011. pp. 81-213

[7] Yang J, Zhang C, Li X, Huang Y, Fu S, Acevedo M. An environmental monitoring system with integrated wired and wireless sensors. In: International Conference on Wireless Algorithms, Systems and Applications;

[8] Flammin A, Ferrari P, Marioli D, Sisinni E. Wired and wireless sensor networks for industrial

applications. Microelectronics Journal.

[9] Kaur N, Monga S. Comparisons of wired and wireless networks: A review. International Journal of Advanced Engineering Technology. 2014;**2**:34-35

A taxonomy of wireless micro sensor network nodels. ACM

Recent development and future

in Agriculture. 2006;**50**:1-14

Journal. 2012;**12**:1965-1972

2011;**26**:2146-2151

2008;**3**:224-236

2009;**40**:1322-1336

**References**

*Smart Environment Monitoring System Using Wired and Wireless Network: A Comparative Study DOI: http://dx.doi.org/10.5772/intechopen.86316*

## **References**

*Atmospheric Air Pollution and Monitoring*

*Front panel showing remote monitoring of a system.*

A lucrative environmental monitoring method with least amount of components has been constructed. The system is successfully developed using wired and wireless networks. The limitations of wired network and opportunities using wireless networks are rigorously described. The environment monitoring sensors with an Atmega 368 microcontroller, Web portal is proposed. For sending and receiving of the data, the web publishing tool in LabVIEW is used. The system is developed using two motes and one sink node. XBee protocol is used to provide wireless access. This system provides a real-time monitoring via money-spinning low data rate and significant power wireless communication technology. We envisage that this system will encompass an enormous recognition in the industrialized sectors and will realize an effective amalgamation among WSN and Web portal. Accordingly, a tack target of inaccessible monitoring of the air quality within the environment can be attained. It is highly pertinent to metrological departments and also in industrial sectors. In future, we would be fond of to be made controlling system for environment monitoring.

**Figure 14.**

**6. Conclusions**

**Author details**

Tabbsum Mujawar\* and Lalasaheb Deshmukh

provided the original work is properly cited.

\*Address all correspondence to: thmujawar@sus.ac.in

School of Physical Sciences, Solapur University, Solapur, Maharashtra, India

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

**84**

[1] Mujawar T. Development of wireless sensor network for hazardous gas detection and alert system [thesis]. Inflibnet. 30 Jan 2017. Available from: http://www.shodhgang.inflibnet.ac.in

[2] Wang N, Zhang N, Wang M. Wireless sensors in agriculture and food industry. Recent development and future perspective. Computers and Electronics in Agriculture. 2006;**50**:1-14

[3] Suryadevara N, Mukhopadhyay S. Wireless sensor network based home monitoring system for wellness determination of elderly. IEEE Sensors Journal. 2012;**12**:1965-1972

[4] Tiantian J, Zhanyong Y. Research on mine safety monitoring system based on WSN. Procedia Engineering. 2011;**26**:2146-2151

[5] Tilak S, Ghazaleh N, Heinzelman W. A taxonomy of wireless micro sensor network nodels. ACM SIGMOBILE Mobile Computing and Communications Review. 2002;**6**:28-36

[6] Margolis M. Arduino Cookbook. 1st ed. Massachusetts, United States: O'REILLY® Media, Inc.; 2011. pp. 81-213

[7] Yang J, Zhang C, Li X, Huang Y, Fu S, Acevedo M. An environmental monitoring system with integrated wired and wireless sensors. In: International Conference on Wireless Algorithms, Systems and Applications; 2008;**3**:224-236

[8] Flammin A, Ferrari P, Marioli D, Sisinni E. Wired and wireless sensor networks for industrial applications. Microelectronics Journal. 2009;**40**:1322-1336

[9] Kaur N, Monga S. Comparisons of wired and wireless networks: A review. International Journal of Advanced Engineering Technology. 2014;**2**:34-35

[10] Mujawar T, Kasbe M, Mule S, Deshmukh L. Development of wireless gas sensing system for home safety. International Journal of Engineering Sciences & Emerging Technologies. 2016;**8**:213-221

[11] Mujawar T, Kasbe M, Mule S, Deshmukh L. Online monitoring of WSN based air quality monitoring system. AIP Conference Proceedings. 2018;**02003**:020030-1-020030-9

[12] Schell M, Guvench M. Development of a general purpose XBee series-2 API-mode communication library for LabVIEW. In: Northeast Section Conference (ASEE); April 27-28, 2012

[13] Zhang J, Song G. Design of a wireless sensor network based monitoring system for home automation. In: International Conference on Future Computer Sciences and Application. June 2011;**7**:18-19

[14] Li Y, Ji M. Design of home automation system based on ZigBee wireless sensor network. In: 1st International Conference on Information Science and Engineering (ICISE); December 26-28, 2009

## *Edited by Abderrahim Lakhouit*

Indoor air quality (IAQ) is an important aspect in building design due to its effect on human health and wellbeing. Generally, people spend about 90% of their time indoors where they are exposed to chemicals, particulate matters, biological contaminants and possibly carcinogens. In particular, the air quality at hospitals carries with it risks for serious health consequences for medical staff as well as patients and visitors. This book is a study of atmospheric air pollution and presents ways we can reduce its impacts on human health. It discusses tools for measuring IAQ as well as analyzes IAQ in closed buildings. It is an important documentation of air quality and its impact on human health.

Published in London, UK © 2020 IntechOpen © Ieuan / iStock

Atmospheric Air Pollution and Monitoring

Atmospheric Air Pollution

and Monitoring

*Edited by Abderrahim Lakhouit*