Spatially dense air pollutant sampling: Implications of spatial variability on the representativeness of stationary air pollutant monitors
Long- and short-term exposure to airborne pollutants results in adverse health effects. Regulatory monitors can be used to determine if regional concentrations meet regulatory standards of air pollution. As assessments of air pollutant exposure become more spatially resolved, evaluation is needed to...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2019-04-01
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| Series: | Atmospheric Environment: X |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590162119300152 |
| _version_ | 1811286011291893760 |
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| author | Hugh Z. Li Peishi Gu Qing Ye Naomi Zimmerman Ellis S. Robinson R. Subramanian Joshua S. Apte Allen L. Robinson Albert A. Presto |
| author_facet | Hugh Z. Li Peishi Gu Qing Ye Naomi Zimmerman Ellis S. Robinson R. Subramanian Joshua S. Apte Allen L. Robinson Albert A. Presto |
| author_sort | Hugh Z. Li |
| collection | DOAJ |
| description | Long- and short-term exposure to airborne pollutants results in adverse health effects. Regulatory monitors can be used to determine if regional concentrations meet regulatory standards of air pollution. As assessments of air pollutant exposure become more spatially resolved, evaluation is needed to assess the spatial representativeness of monitors in different environments. We measured NO2, ultrafine particle concentration (UFP), and PM1 with both stationary and mobile platforms in Pittsburgh, PA in 2016 and 2017. We sampled in eight ∼1 km2 neighborhoods representing different land use and exposure regimes (e.g., urban and suburban, high and low traffic). Mobile sampling was conducted on up to 25 days in each neighborhood to study fine-scale spatial variation in pollutant concentrations. NO2 exhibited within-neighborhood spatial variation, with hotspots elevated by up to a factor of 5 above the regional background. Spatial differences in UFP within the same 1 km2 neighborhoods could be a factor of 2.4 times regional background. PM1 was more regional and less spatially variable. Most neighborhoods exhibited less than 1 μg m−3 spatial variability in PM1. Spatial variability of NO2 and UFP showed moderate correlation (R2 > 0.5) with traditional land use covariates such as traffic volume and restaurant density. We used the Wilcoxon rank-sum test to calculate the fraction of each neighborhood represented by the same underlying concentration distribution. PM1 was the most spatially homogeneous, with 80–100% of each 1 km2 area being statistically similar to a reference location. Quantifying pollutant spatial patterns with high fidelity (e.g., <2 ppb NO2 or <1 μg m−3 PM1) seems unattainable in many urban areas unless the sampling network is significantly dense, with more than one or two nodes per km2. Keywords: Particulate matter, Exposure, Low-cost sensors, Spatial variations |
| first_indexed | 2024-04-13T02:53:21Z |
| format | Article |
| id | doaj.art-8fba7b6824a9448aa481955b39534c7b |
| institution | Directory Open Access Journal |
| issn | 2590-1621 |
| language | English |
| last_indexed | 2024-04-13T02:53:21Z |
| publishDate | 2019-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Atmospheric Environment: X |
| spelling | doaj.art-8fba7b6824a9448aa481955b39534c7b2022-12-22T03:05:47ZengElsevierAtmospheric Environment: X2590-16212019-04-012Spatially dense air pollutant sampling: Implications of spatial variability on the representativeness of stationary air pollutant monitorsHugh Z. Li0Peishi Gu1Qing Ye2Naomi Zimmerman3Ellis S. Robinson4R. Subramanian5Joshua S. Apte6Allen L. Robinson7Albert A. Presto8Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA, 15213, United StatesCenter for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA, 15213, United StatesCenter for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA, 15213, United StatesCenter for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA, 15213, United StatesCenter for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA, 15213, United StatesCenter for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA, 15213, United StatesDepartment of Civil, Architectural and Environmental Engineering, The University of Texas at Austin, Austin, TX, 78712, United StatesCenter for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA, 15213, United StatesCenter for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA, 15213, United States; Corresponding author.Long- and short-term exposure to airborne pollutants results in adverse health effects. Regulatory monitors can be used to determine if regional concentrations meet regulatory standards of air pollution. As assessments of air pollutant exposure become more spatially resolved, evaluation is needed to assess the spatial representativeness of monitors in different environments. We measured NO2, ultrafine particle concentration (UFP), and PM1 with both stationary and mobile platforms in Pittsburgh, PA in 2016 and 2017. We sampled in eight ∼1 km2 neighborhoods representing different land use and exposure regimes (e.g., urban and suburban, high and low traffic). Mobile sampling was conducted on up to 25 days in each neighborhood to study fine-scale spatial variation in pollutant concentrations. NO2 exhibited within-neighborhood spatial variation, with hotspots elevated by up to a factor of 5 above the regional background. Spatial differences in UFP within the same 1 km2 neighborhoods could be a factor of 2.4 times regional background. PM1 was more regional and less spatially variable. Most neighborhoods exhibited less than 1 μg m−3 spatial variability in PM1. Spatial variability of NO2 and UFP showed moderate correlation (R2 > 0.5) with traditional land use covariates such as traffic volume and restaurant density. We used the Wilcoxon rank-sum test to calculate the fraction of each neighborhood represented by the same underlying concentration distribution. PM1 was the most spatially homogeneous, with 80–100% of each 1 km2 area being statistically similar to a reference location. Quantifying pollutant spatial patterns with high fidelity (e.g., <2 ppb NO2 or <1 μg m−3 PM1) seems unattainable in many urban areas unless the sampling network is significantly dense, with more than one or two nodes per km2. Keywords: Particulate matter, Exposure, Low-cost sensors, Spatial variationshttp://www.sciencedirect.com/science/article/pii/S2590162119300152 |
| spellingShingle | Hugh Z. Li Peishi Gu Qing Ye Naomi Zimmerman Ellis S. Robinson R. Subramanian Joshua S. Apte Allen L. Robinson Albert A. Presto Spatially dense air pollutant sampling: Implications of spatial variability on the representativeness of stationary air pollutant monitors Atmospheric Environment: X |
| title | Spatially dense air pollutant sampling: Implications of spatial variability on the representativeness of stationary air pollutant monitors |
| title_full | Spatially dense air pollutant sampling: Implications of spatial variability on the representativeness of stationary air pollutant monitors |
| title_fullStr | Spatially dense air pollutant sampling: Implications of spatial variability on the representativeness of stationary air pollutant monitors |
| title_full_unstemmed | Spatially dense air pollutant sampling: Implications of spatial variability on the representativeness of stationary air pollutant monitors |
| title_short | Spatially dense air pollutant sampling: Implications of spatial variability on the representativeness of stationary air pollutant monitors |
| title_sort | spatially dense air pollutant sampling implications of spatial variability on the representativeness of stationary air pollutant monitors |
| url | http://www.sciencedirect.com/science/article/pii/S2590162119300152 |
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