3. Results

#### 3.1 Micropollutant monitoring

To complement the micropollutant data analysis and to enable a more comprehensive study of micropollutant sources, we first collected geospatial data for the Hudson River Estuary catchment area. We used ArcGIS and publically available data to develop maps of the Hudson River Estuary catchment area that include

geospatial references for land cover, industrial discharge locations, sewage outfalls, and hospitals. We expected that the occurrence and concentrations of certain types of micropollutants would be associated with the geographic distances to these types of catchment features. For example, a recent geospatial analysis of poly- and perfluoroalkyl substances (PFASs) revealed that PFASs were found at higher concentrations in more urban areas and different types of PFASs were associated with different point sources such as airports, textile mills, and metal smelting [1]. Another recent study used spatial analysis techniques to predict mass flows and concentrations of pharmaceuticals in surface water samples using hospital locations, departments, and the number of beds [32]. These examples demonstrate powerful ways in which geospatial data can be combined with micropollutant occurrence data to improve our fundamental understanding of micropollutant sources. We collected grab samples in May, July, and September 2016 from the 17 locations along the Hudson River Estuary. The sample collected in May 2016 from Rondout Creek-Kingston STP Outfall was lost during sample shipment; therefore, a total of 50 samples were processed and analyzed in our laboratory. Our target list was comprised of 200 total micropollutants which included 134 wastewater-derived compounds (pharmaceuticals, industrial compounds, personal care products, hormones, food additives, and illicit drugs) and 66 pesticides (including herbicides, insecticides, and fungicides). From our target list, 160 of the micropollutants were detected in at least 1 of the 50 samples; 111 were wastewater-derived compounds and 49 were pesticides. Figure 3 presents the distribution of detected micropollutants by use class. Twelve of the 200 micropollutants were detected in all 50 samples, with an additional 25 being detected in at least 40 samples. The micropollutants detected in all samples included acesulfame (artificial sweetener), atenolol acid (metabolite of atenolol and metoprolol), atrazine (herbicide), benzotriazole-methyl-1H (corrosion inhibitor), carbamazepine (antiepileptic), DEET (insect repellent), gabapentin (antiepileptic), lamotrigine (anticonvulsant), metolachlor (herbicide), sucralose (artificial sweetener), desvenlafaxine (metabolite of the antidepressant venlafaxine), and valsartan (angiotensin II antagonist). The highest occurrence and concentrations of all micropollutants were detected in the STP outfall samples. Sucralose, atenolol acid, and metformin (antidiabetic) were detected at the highest concentrations in the mid-mg L<sup>1</sup> range. It must be noted that data derived from grab samples do not necessarily reflect the expected dynamics of micropollutant occurrence or concentration in surface water systems [33] However, longer time series of grab samples can be analyzed to provide more robust estimates of the likelihood of occurrence and average concentrations of specific micropollutants at a particular sample site. The majority of micropollutants that were detected were measured in the 1–100 ng L<sup>1</sup> range.

We next aimed to investigate how the micropollutant occurrence profiles (defined as the occurrence of individual micropollutants in a sample) compared among the 17 sample sites. To do this, we used Ward's method to cluster each micropollutant based on its spatiotemporal occurrence pattern. The resulting dendrogram is presented in Figure 4 and reveals four distinct micropollutant clusters that describe the relationship in spatiotemporal occurrence among the micropollutants. Cluster 1 contains 56 micropollutants that are present in most samples, regardless of sample type (tributaries, control sites, and STP outfalls) or sample date. Cluster 2 contains 28 micropollutants that were detected most frequently in the tributary and control sites but rarely in the STP outfalls. Cluster 3 contains 33 micropollutants that were detected more often in the STP outfalls than the tributary or control sites. Cluster 4 contains 43 micropollutants that were detected mostly in STP outfalls and in 2 separate tributary samples collected on different dates.

We next aimed to identify whether STP outfalls or specific tributaries were important sources of different spatiotemporal occurrence clusters to the Hudson River. To determine the relative contributions of each cluster of micropollutants to the Hudson River, we compared the micropollutant concentrations from samples taken inside the tributaries to the micropollutant concentrations measured in the midchannel control samples. Then, we examined our geospatial datasets for associations between micropollutant concentrations and different types of catchment features. It is important to note here that concentration data obtained from grab samples may not accurately assess the influence of specific tributaries to the Hudson River due to variability in stream flowrates. In that respect, micropollutant loads are

Dendrogram of micropollutants (n = 160) clustered by spatiotemporal occurrence patterns in all samples.

The number of use classes of micropollutants detected in at least one of the 50 samples taken; 111 were

Water Quality in the Twenty-First Century: New Tools for the Characterization…

DOI: http://dx.doi.org/10.5772/intechopen.90099

wastewater-derived compounds and 49 pesticides were detected.

Figure 3.

Figure 4.

33

Water Quality in the Twenty-First Century: New Tools for the Characterization… DOI: http://dx.doi.org/10.5772/intechopen.90099

#### Figure 3.

geospatial references for land cover, industrial discharge locations, sewage outfalls, and hospitals. We expected that the occurrence and concentrations of certain types of micropollutants would be associated with the geographic distances to these types of catchment features. For example, a recent geospatial analysis of poly- and perfluoroalkyl substances (PFASs) revealed that PFASs were found at higher concentrations in more urban areas and different types of PFASs were associated with different point sources such as airports, textile mills, and metal smelting [1]. Another recent study used spatial analysis techniques to predict mass flows and concentrations of pharmaceuticals in surface water samples using hospital locations, departments, and the number of beds [32]. These examples demonstrate powerful ways in which geospatial data can be combined with micropollutant occurrence data to improve our fundamental understanding of micropollutant sources. We collected grab samples in May, July, and September 2016 from the 17 locations along the Hudson River Estuary. The sample collected in May 2016 from Rondout Creek-Kingston STP Outfall was lost during sample shipment; therefore, a total of 50 samples were processed and analyzed in our laboratory. Our target list was comprised of 200 total micropollutants which included 134 wastewater-derived com-

Technology, Science and Culture - A Global Vision, Volume II

pounds (pharmaceuticals, industrial compounds, personal care products,

and 49 were pesticides. Figure 3 presents the distribution of detected

that were detected were measured in the 1–100 ng L<sup>1</sup> range.

different dates.

32

We next aimed to investigate how the micropollutant occurrence profiles (defined as the occurrence of individual micropollutants in a sample) compared among the 17 sample sites. To do this, we used Ward's method to cluster each micropollutant based on its spatiotemporal occurrence pattern. The resulting dendrogram is presented in Figure 4 and reveals four distinct micropollutant clusters

micropollutants. Cluster 1 contains 56 micropollutants that are present in most samples, regardless of sample type (tributaries, control sites, and STP outfalls) or sample date. Cluster 2 contains 28 micropollutants that were detected most frequently in the tributary and control sites but rarely in the STP outfalls. Cluster 3 contains 33 micropollutants that were detected more often in the STP outfalls than the tributary or control sites. Cluster 4 contains 43 micropollutants that were detected mostly in STP outfalls and in 2 separate tributary samples collected on

that describe the relationship in spatiotemporal occurrence among the

50 samples, with an additional 25 being detected in at least 40 samples. The micropollutants detected in all samples included acesulfame (artificial sweetener), atenolol acid (metabolite of atenolol and metoprolol), atrazine (herbicide), benzotriazole-methyl-1H (corrosion inhibitor), carbamazepine (antiepileptic), DEET (insect repellent), gabapentin (antiepileptic), lamotrigine (anticonvulsant), metolachlor (herbicide), sucralose (artificial sweetener), desvenlafaxine (metabolite of the antidepressant venlafaxine), and valsartan (angiotensin II antagonist). The highest occurrence and concentrations of all micropollutants were detected in the STP outfall samples. Sucralose, atenolol acid, and metformin (antidiabetic) were detected at the highest concentrations in the mid-mg L<sup>1</sup> range. It must be noted that data derived from grab samples do not necessarily reflect the expected dynamics of micropollutant occurrence or concentration in surface water systems [33] However, longer time series of grab samples can be analyzed to provide more robust estimates of the likelihood of occurrence and average concentrations of specific micropollutants at a particular sample site. The majority of micropollutants

hormones, food additives, and illicit drugs) and 66 pesticides (including herbicides, insecticides, and fungicides). From our target list, 160 of the micropollutants were detected in at least 1 of the 50 samples; 111 were wastewater-derived compounds

micropollutants by use class. Twelve of the 200 micropollutants were detected in all

The number of use classes of micropollutants detected in at least one of the 50 samples taken; 111 were wastewater-derived compounds and 49 pesticides were detected.

#### Figure 4.

Dendrogram of micropollutants (n = 160) clustered by spatiotemporal occurrence patterns in all samples.

We next aimed to identify whether STP outfalls or specific tributaries were important sources of different spatiotemporal occurrence clusters to the Hudson River. To determine the relative contributions of each cluster of micropollutants to the Hudson River, we compared the micropollutant concentrations from samples taken inside the tributaries to the micropollutant concentrations measured in the midchannel control samples. Then, we examined our geospatial datasets for associations between micropollutant concentrations and different types of catchment features. It is important to note here that concentration data obtained from grab samples may not accurately assess the influence of specific tributaries to the Hudson River due to variability in stream flowrates. In that respect, micropollutant loads are a more representative metric of overall contribution to the Hudson River. Nevertheless, concentration data enabled us to preliminarily identify specific tributaries that are likely important sources of micropollutants to the Hudson River.

each adsorbent. To enable a more robust interpretation of micropollutant affinity for each adsorbent, we measured the percent removal of each micropollutant after 5 min for CCAC and P-CDP and summarize the data in Figure 6. Despite the faster

micropollutant uptake at 5 min is more evenly split between the two adsorbents, which we attribute to differences in the affinities of each micropollutant for each adsorbent under the experimental conditions. Whereas most micropollutants have moderate affinity for CCAC, the distribution of micropollutant affinity for P-CDP is more variable, with some micropollutants having very strong affinity (more than 80% removal) and others having relatively weak affinity (removal less than 20%). Overall, these data from the batch experiments demonstrate that micropollutant uptake on P-CDP is generally rapid but selective, with some micropollutants attaining complete uptake in 5 min and others being removed to only minor extents. In contrast, CCAC exhibits relatively slow uptake kinetics, though uptake is rather nonselective with increasing extents of uptake of most micropollutants over time. We also characterized the performance of P-CDP as an adsorbent by evaluating the instantaneous removal of micropollutants in flow-through experiments designed to simulate filtration-type adsorption processes. The same mixture of 90 micropollutants was pushed through thin layers of each adsorbent immobilized on a nonadsorbent membrane at a constant flow rate. Experiments were conducted with an adsorbent dose of 1 mg. Differences in measured concentrations before and after filtration were used to calculate the removal of each micropollutant in each experiment. The removal percentages of each micropollutant on each adsorbent are summarized in Figure 7. A total of 47 micropollutants were removed to greater extents by P-CDP in flowthrough experiments designed to simulate filtration-type adsorption processes. This provides another example of how the rapid adsorption kinetics exhibited by P-CDP can lead to significant removal of micropollutants even in processes providing limited contact time. These data again suggest a selectivity to micropollutant uptake on P-CDP. Finally, despite this selectivity, it is important to emphasize that the micropollutants that are efficiently removed by P-CDP are nearly completely removed in the flow-through experiments; 24 of the micropollutants exhibit greater than 95% removal in these experiments, whereas only 1 micropollutant is removed

Comparison of the percent removal of each micropollutant after 5 min contact time with either P-CDP or CCAC.

adsorption kinetics exhibited by P-CDP relative to CCAC, the extent of

Water Quality in the Twenty-First Century: New Tools for the Characterization…

DOI: http://dx.doi.org/10.5772/intechopen.90099

to that extent by CCAC.

Figure 6.

35

Rondout Creek and Normans Kill were identified as the major contributors of wastewater-derived micropollutants to the Hudson River Estuary. Rondout Creek was also identified as a major contributor of agricultural micropollutants. Our analysis confirms that the Hudson River Estuary is more impacted by micropollutants as it flows south toward New York City. Additionally, our geospatial analysis revealed several associations between the spatiotemporal occurrence clusters and certain geographic catchment features including the extent of total agricultural land cover, extent of cultivated cropland land cover, number of the major STP outfalls, and hydraulic distances to the major STP outfalls. It is important to note that while this sampling campaign had high spatial resolution and a large number of targeted micropollutants, it has low temporal resolution with only three separate grab sampling events during the 2016 recreational season. Largescale sampling campaigns such as these can benefit from higher temporal resolution to gain a more representative understanding of micropollutant concentrations and increase the power of the statistical tests.

#### 3.2 Micropollutant adsorption

Data from the batch experiments were first evaluated to estimate pseudosecond-order adsorption rate constants (kobs) for each micropollutant and each adsorbent. The estimated values of kobs for each micropollutant on each adsorbent are summarized in Figure 5. Generally, if a kobs could be estimated from the data for a particular micropollutant, its value was significantly greater on P-CDP than CCAC. These data corroborate our earlier observations of nearly instantaneous equilibrium adsorption of several model organic molecules on P-CDP [25]. The rapid micropollutant uptake by P-CDP is attributed to the accessibility of the β-CD binding sites in the polymer due to its porosity and high surface area.

The estimated values of kobs describe the rate at which adsorption equilibrium is attained but do not provide any insight on the affinity of each micropollutant for

#### Figure 5.

Comparison of pseudo-second-order rate constants (kobs) for the adsorption of each micropollutant by P-CDP and CCAC.

#### Water Quality in the Twenty-First Century: New Tools for the Characterization… DOI: http://dx.doi.org/10.5772/intechopen.90099

each adsorbent. To enable a more robust interpretation of micropollutant affinity for each adsorbent, we measured the percent removal of each micropollutant after 5 min for CCAC and P-CDP and summarize the data in Figure 6. Despite the faster adsorption kinetics exhibited by P-CDP relative to CCAC, the extent of micropollutant uptake at 5 min is more evenly split between the two adsorbents, which we attribute to differences in the affinities of each micropollutant for each adsorbent under the experimental conditions. Whereas most micropollutants have moderate affinity for CCAC, the distribution of micropollutant affinity for P-CDP is more variable, with some micropollutants having very strong affinity (more than 80% removal) and others having relatively weak affinity (removal less than 20%). Overall, these data from the batch experiments demonstrate that micropollutant uptake on P-CDP is generally rapid but selective, with some micropollutants attaining complete uptake in 5 min and others being removed to only minor extents. In contrast, CCAC exhibits relatively slow uptake kinetics, though uptake is rather nonselective with increasing extents of uptake of most micropollutants over time.

We also characterized the performance of P-CDP as an adsorbent by evaluating the instantaneous removal of micropollutants in flow-through experiments designed to simulate filtration-type adsorption processes. The same mixture of 90 micropollutants was pushed through thin layers of each adsorbent immobilized on a nonadsorbent membrane at a constant flow rate. Experiments were conducted with an adsorbent dose of 1 mg. Differences in measured concentrations before and after filtration were used to calculate the removal of each micropollutant in each experiment. The removal percentages of each micropollutant on each adsorbent are summarized in Figure 7.

A total of 47 micropollutants were removed to greater extents by P-CDP in flowthrough experiments designed to simulate filtration-type adsorption processes. This provides another example of how the rapid adsorption kinetics exhibited by P-CDP can lead to significant removal of micropollutants even in processes providing limited contact time. These data again suggest a selectivity to micropollutant uptake on P-CDP. Finally, despite this selectivity, it is important to emphasize that the micropollutants that are efficiently removed by P-CDP are nearly completely removed in the flow-through experiments; 24 of the micropollutants exhibit greater than 95% removal in these experiments, whereas only 1 micropollutant is removed to that extent by CCAC.

Figure 6. Comparison of the percent removal of each micropollutant after 5 min contact time with either P-CDP or CCAC.

a more representative metric of overall contribution to the Hudson River. Nevertheless, concentration data enabled us to preliminarily identify specific tributaries

Rondout Creek and Normans Kill were identified as the major contributors of wastewater-derived micropollutants to the Hudson River Estuary. Rondout Creek was also identified as a major contributor of agricultural micropollutants. Our

geospatial analysis revealed several associations between the spatiotemporal occurrence clusters and certain geographic catchment features including the extent of total agricultural land cover, extent of cultivated cropland land cover, number of the major STP outfalls, and hydraulic distances to the major STP outfalls. It is important to note that while this sampling campaign had high spatial resolution and a large number of targeted micropollutants, it has low temporal resolution with only three separate grab sampling events during the 2016 recreational season. Largescale sampling campaigns such as these can benefit from higher temporal resolution to gain a more representative understanding of micropollutant concentrations and

Data from the batch experiments were first evaluated to estimate pseudosecond-order adsorption rate constants (kobs) for each micropollutant and each adsorbent. The estimated values of kobs for each micropollutant on each adsorbent are summarized in Figure 5. Generally, if a kobs could be estimated from the data for a particular micropollutant, its value was significantly greater on P-CDP than CCAC. These data corroborate our earlier observations of nearly instantaneous equilibrium adsorption of several model organic molecules on P-CDP [25]. The rapid micropollutant uptake by P-CDP is attributed to the accessibility of the β-CD

The estimated values of kobs describe the rate at which adsorption equilibrium is attained but do not provide any insight on the affinity of each micropollutant for

Comparison of pseudo-second-order rate constants (kobs) for the adsorption of each micropollutant by P-CDP

binding sites in the polymer due to its porosity and high surface area.

that are likely important sources of micropollutants to the Hudson River.

Technology, Science and Culture - A Global Vision, Volume II

analysis confirms that the Hudson River Estuary is more impacted by micropollutants as it flows south toward New York City. Additionally, our

increase the power of the statistical tests.

3.2 Micropollutant adsorption

Figure 5.

and CCAC.

34

0.78-nm-diameter interior cavity [26]. Host-guest complex formation requires that organic molecules fit inside the interior cavity of β-CD and large organic molecules do not bind well with β-CD, presumably due to a size exclusion mechanism. This result is particularly exciting because it suggests that P-CDP might not be fouled by NOM in natural waters, instead reserving its binding sites for smaller organic molecules. Remarkably, 75 micropollutants were removed to greater extents by P-CDP in the presence of matrix constituents. Of those, 44 were removed to greater than 80% on P-CDP, whereas only 13 micropollutants were removed to greater than

Water Quality in the Twenty-First Century: New Tools for the Characterization…

DOI: http://dx.doi.org/10.5772/intechopen.90099

The first aim of this research was to improve our understanding of the sources of micropollutants in the Hudson River Estuary. We collected samples from 17 locations along the Hudson River Estuary during May, July, and September 2016. The sample locations were selected to target sewage treatment plant (STP) outfalls and tributaries that are expected to be the major sources of micropollutants in the Hudson River. The samples were analyzed to quantify the occurrence of 200 wastewater-derived micropollutants and pesticides. The data was analyzed to identify the relative contributions of various sources of micropollutants and specific outfalls or tributaries that are significant sources of micropollutants in the Hudson River Estuary and revealed four distinct clusters of micropollutants grouped by their occurrence profiles. Rondout Creek and Normans Kill were both identified as the major contributors of wastewater-derived micropollutants to the Hudson River Estuary. Rondout Creek was also identified as a major contributor of agricultural micropollutants. Our geospatial analysis revealed several associations between the spatiotemporal occurrence clusters and certain geographic catchment features including the extent of total agricultural land cover, extent of cultivated cropland land cover, number of the major STP outfalls, and hydraulic distances to the major STP outfalls. These data can be used to develop targeted micropollutant mitigation strategies in the Hudson River Estuary. An expanded survey of micropollutants in the Hudson River Estuary that contains the data presented here has been published

The second aim of this research was to study cost-effective and energy-efficient technologies to enhance the removal of micropollutants in water and wastewater treatment systems. Despite their expense, AC adsorption processes have emerged as a leading alternative, though they are limited by relatively slow adsorption kinetics and a tendency to become fouled by NOM and other matrix constituents. Our results suggest that β-cyclodextrin polymer adsorbents address these specific deficiencies and therefore might be developed into a viable alternative or complementary adsorbent in water and wastewater treatment. Further, β-cyclodextrin polymer adsorbents are prepared in a single step from commercially available monomers, including the commodity chemical β-CD. Because it is synthesized through a rational process, many related compositions of β-cyclodextrin polymer adsorbents can be designed to target improved performance or different selectivity. These factors make it possible that β-cyclodextrin polymer adsorbents might be produced at large scales and deployed with competitive life cycle costs to ACs used in water and wastewater treatment. Together, these features all suggest that β-cyclodextrin polymer adsorbents may be a promising alternative adsorbent for the removal of micropollutants during water and wastewater treatment. An expanded study of micropollutant adsorption on cyclodextrin polymers has been published in the peer-

80% on CCAC in the presence of NOM and matrix constituents.

4. Conclusions

in the peer-reviewed literature [36].

reviewed literature [37].

37

#### Figure 7.

Comparison of the removal percentages by P-CDP and CCAC measured for each micropollutant from flow-through experiments.

One of the main deficiencies of CCAC as an adsorbent is its tendency to be fouled by NOM and other matrix constituents [23]. Therefore, we evaluated the performance of both adsorbents in the presence of NOM and inorganic ions. We repeated the flow-through experiments in the presence of 20 mg L<sup>1</sup> of humic acid (HA, as a surrogate for NOM) and 200 mg L<sup>1</sup> of NaCl to simulate the conditions in a typical surface water system. The removal percentages of each micropollutant are summarized in Figure 8. As expected, the addition of matrix constituents had a significant negative influence on the adsorption of micropollutants to the CCAC, likely as the result of a direct site competition or pore blockage mechanism [34, 35]. In contrast, no significant negative effect was observed for P-CDP. This was not necessarily unexpected; the binding sites of β-CD are contained inside its

#### Figure 8.

Comparison of the removal percentages by P-CDP and CCAC measured for each micropollutant from flowthrough experiments conducted with added matrix constituents (20 mg L<sup>1</sup> NOM and 200 mg L<sup>1</sup> NaCl).

Water Quality in the Twenty-First Century: New Tools for the Characterization… DOI: http://dx.doi.org/10.5772/intechopen.90099

0.78-nm-diameter interior cavity [26]. Host-guest complex formation requires that organic molecules fit inside the interior cavity of β-CD and large organic molecules do not bind well with β-CD, presumably due to a size exclusion mechanism. This result is particularly exciting because it suggests that P-CDP might not be fouled by NOM in natural waters, instead reserving its binding sites for smaller organic molecules. Remarkably, 75 micropollutants were removed to greater extents by P-CDP in the presence of matrix constituents. Of those, 44 were removed to greater than 80% on P-CDP, whereas only 13 micropollutants were removed to greater than 80% on CCAC in the presence of NOM and matrix constituents.
