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Terry B, Shakya KM. Monitoring gaseous pollutants using passive sampling in the Philadelphia region. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION (1995) 2024; 74:52-69. [PMID: 37934867 DOI: 10.1080/10962247.2023.2279733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 10/23/2023] [Indexed: 11/09/2023]
Abstract
Air pollution can have deleterious impacts on human health and the environment. Historically, air pollution studies have focused more on cities. However, it is also important to consider the impact on large suburban populations living closer to the major cities. In this study, nitrogen oxides (nitrogen dioxide and nitric oxide), sulfur dioxide, ozone, and ammonia concentrations were measured from fifteen sites in the Greater Philadelphia area, Pennsylvania, USA using Ogawa passive samplers from September 2021 to May 2022. The fall season had the highest mean NOx concentrations (11.03 ± 4.51 ppb), and spring had the highest mean O3 concentration (18.65 ± 6.71 ppb) compared to other seasons. NOx concentrations were higher at suburban (30.43 ± 33.79 ppb) and urban sites (22.49 ± 12.54 ppb) compared to semi-rural sites (11.08 ± 9.20 ppb). SO2 was not detected in most of the measurements. The positive statistically significant correlation between NO and NH3 in urban (R2 = 0.33, p-value <0.05) and suburban sites (R2 = 0.37, p-value <0.05) during winter and spring, respectively, suggests a high attribution of traffic emissions to NH3 at urban and suburban sites. Influence of traffic emissions on air pollutant values for the study region is also supported by similar NOx concentrations between suburban and urban sites as well as decreasing NO2/NOx ratios with increased distance from expressways. This study shows that passive sampling can be effectively used for assessing spatial and seasonal variations in air pollutants within an area of diverse land use.Implications: This study presents the findings of temporal and seasonal patterns for nitrogen dioxide, nitric oxide, tropospheric ozone, and ammonia at urban, suburban, and semi-rural areas of the greater Philadelphia region. The main objective of the study is to monitor air pollution in suburban and semi-rural areas which are not monitored for air pollution. We monitored from a total of fifteen sites in three seasons to assess air pollution in suburban and semi-rural areas near the major city in the United States - Philadelphia. The findings are important to learn how air quality is affected in suburban and semi-rural areas near the major city. The study also shows the useful application of inexpensive passive sampling technique for measuring air pollution.
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Affiliation(s)
- Bryan Terry
- Department of Geography and the Environment, Villanova University, Villanova, Pennsylvania, USA
| | - Kabindra M Shakya
- Department of Geography and the Environment, Villanova University, Villanova, Pennsylvania, USA
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Kim SY, Blanco MN, Bi J, Larson TV, Sheppard L. Exposure assessment for air pollution epidemiology: A scoping review of emerging monitoring platforms and designs. ENVIRONMENTAL RESEARCH 2023; 223:115451. [PMID: 36764437 PMCID: PMC9992293 DOI: 10.1016/j.envres.2023.115451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 01/10/2023] [Accepted: 02/07/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Both exposure monitoring and exposure prediction have played key roles in assessing individual-level long-term exposure to air pollutants and their associations with human health. While there have been notable advances in exposure prediction methods, improvements in monitoring designs are also necessary, particularly given new monitoring paradigms leveraging low-cost sensors and mobile platforms. OBJECTIVES We aim to provide a conceptual summary of novel monitoring designs for air pollution cohort studies that leverage new paradigms and technologies, to investigate their characteristics in real-world examples, and to offer practical guidance to future studies. METHODS We propose a conceptual summary that focuses on two overarching types of monitoring designs, mobile and non-mobile, as well as their subtypes. We define mobile designs as monitoring from a moving platform, and non-mobile designs as stationary monitoring from permanent or temporary locations. We only consider non-mobile studies with cost-effective sampling devices. Then we discuss similarities and differences across previous studies with respect to spatial and temporal representation, data comparability between design classes, and the data leveraged for model development. Finally, we provide specific suggestions for future monitoring designs. RESULTS Most mobile and non-mobile monitoring studies selected monitoring sites based on land use instead of residential locations, and deployed monitors over limited time periods. Some studies applied multiple design and/or sub-design classes to the same area, time period, or instrumentation, to allow comparison. Even fewer studies leveraged monitoring data from different designs to improve exposure assessment by capitalizing on different strengths. In order to maximize the benefit of new monitoring technologies, future studies should adopt monitoring designs that prioritize residence-based site selection with comprehensive temporal coverage and leverage data from different designs for model development in the presence of good data compatibility. DISCUSSION Our conceptual overview provides practical guidance on novel exposure assessment monitoring for epidemiological applications.
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Affiliation(s)
- Sun-Young Kim
- Department of Cancer AI and Digital Health, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea; Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA.
| | - Magali N Blanco
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA
| | - Jianzhao Bi
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA
| | - Timothy V Larson
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA; Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA
| | - Lianne Sheppard
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA; Department of Biostatistics, University of Washington, Seattle, WA, USA
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A Study on the Determination Methods of Monitoring Point for Inundation Damage in Urban Area Using UAV and Hydrological Modeling. WATER 2022. [DOI: 10.3390/w14071117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Recently, unmanned aerial vehicles (UAVs) have been used in various fields, such as military, logistics, transportation, construction, and agriculture, making it possible to apply the limited activities of humans to various and wide ranges. In addition, UAVs have been utilized to construct topographic data that are more precise than existing satellite images or cadastral maps. In this study, a monitoring point for preventing flood damage in an urban area was selected using a UAV. In addition, the topographic data were constructed using a UAV, and the flow of rainwater was examined using the watershed analysis in an urban area. An orthomosaic, a digital surface model (DSM), and a three-dimensional (3D) model were constructed for the topographic data, and a precision of 0.051 m based on the root mean square error (RMSE) was achieved through the observation of ground control points (GCPs). On the other hand, for the watershed analysis in the urban area, the point in which the flow of rainwater converged was analyzed by adjusting the thresholds. A monitoring point for preventing flood damage was proposed by examining the topographic characteristics of the target area related to the inflow of rainwater.
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Song J, Stettler MEJ. A novel multi-pollutant space-time learning network for air pollution inference. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 811:152254. [PMID: 34902415 DOI: 10.1016/j.scitotenv.2021.152254] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 11/12/2021] [Accepted: 12/04/2021] [Indexed: 06/14/2023]
Abstract
Detailed information about air pollution in space and time is essential to manage risks to public health. In this paper we propose a multi-pollutant space-time learning network (Multi-AP learning network), which estimates pixel-wise (grid-level) concentrations of multiple air pollutant species based on fixed-station measurements and multi-source urban features, including land use information, traffic data, and meteorological conditions. We infer concentrations of multiple pollutants within one integrated learning network, which is applied to and evaluated on a case study in Chengdu (4900 km2, 26 April - 12 June 2019), where air pollutant (PM2.5, PM10 and O3) measurements from 40 monitoring sites are used to train the network to estimate pollutant concentrations in 4900 grid-cells (1 km2). The Multi-AP learning network allows us to estimate highly-resolved (1 km × 1 km, hourly) air pollution maps based on pollutant measurements which cover less than 1% of the grid-cells with better accuracy compared to other approaches, and with significant computational efficiency improvements. The time-cost is 1/3 of the time-cost of modelling each pollutant individually. Furthermore, we evaluate the relative importance of features and find that the meteorological feature set is the most important followed the land use features. The proposed Multi-AP method could be used to estimate air pollution exposure across a city using a limited set of air pollution monitoring sites.
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Affiliation(s)
- Jun Song
- Department of Civil and Environmental Engineering, Imperial College London, London, UK
| | - Marc E J Stettler
- Department of Civil and Environmental Engineering, Imperial College London, London, UK.
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Shairsingh KK, Brook JR, Mihele CM, Evans GJ. Characterizing long-term NO 2 concentration surfaces across a large metropolitan area through spatiotemporal land use regression modelling of mobile measurements. ENVIRONMENTAL RESEARCH 2021; 196:111010. [PMID: 33716024 DOI: 10.1016/j.envres.2021.111010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 01/12/2021] [Accepted: 03/08/2021] [Indexed: 06/12/2023]
Abstract
A spatiotemporal land use regression (LUR) model optimized to predict nitrogen dioxide (NO2) concentrations obtained from on-road, mobile measurements collected in 2015-16 was independently evaluated using concentrations observed at multiple sites across Toronto, Canada, obtained more than ten years earlier. This spatiotemporal LUR modelling approach improves upon estimates of historical NO2 concentrations derived from the previously used method of back-extrapolation. The optimal spatiotemporal LUR model (R2 = 0.71 for prediction of NO2 data in 2002 and 2004) uses daily average NO2 concentrations observed at multiple long-term monitoring sites and hourly average wind speed recorded at a single site, along with spatial predictors based on geographical information system data, to estimate NO2 levels for time periods outside of those used for model development. While the model tended to underestimate samplers located close to the roadway, it showed great accuracy when estimating samplers located beyond 100 m which are probably more relevant for exposure at residences. This study shows that spatiotemporal LUR models developed from strategic, multi-day (30 days in 3 different months) mobile measurements can enhance LUR model's ability to estimate long-term, intra-urban NO2 patterns. Furthermore, the mobile sampling strategy enabled this new LUR model to cover a larger domain of Toronto and outlying suburban communities, thereby increasing the potential population for future epidemiological studies.
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Affiliation(s)
- Kerolyn K Shairsingh
- Department of Chemical Engineering and Applied Chemistry. University of Toronto, Toronto, Ontario, M5S 3E5, Canada.
| | - Jeffrey R Brook
- Department of Chemical Engineering and Applied Chemistry. University of Toronto, Toronto, Ontario, M5S 3E5, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, M5T 3M7, Canada.
| | - Cristian M Mihele
- Environment and Climate Change Canada, North York, Ontario, M3H 5T4, Canada
| | - Greg J Evans
- Department of Chemical Engineering and Applied Chemistry. University of Toronto, Toronto, Ontario, M5S 3E5, Canada
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Xu X, Qin N, Yang Z, Liu Y, Cao S, Zou B, Jin L, Zhang Y, Duan X. Potential for developing independent daytime/nighttime LUR models based on short-term mobile monitoring to improve model performance. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 268:115951. [PMID: 33162219 DOI: 10.1016/j.envpol.2020.115951] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 10/10/2020] [Accepted: 10/27/2020] [Indexed: 06/11/2023]
Abstract
Land use regression model (LUR) is a widespread method for predicting air pollution exposure. Few studies have explored the performance of independently developed daytime/nighttime LUR models. In this study, fine particulate matter (PM2.5), inhalable particulate matter (PM10), and nitrogen dioxide (NO2) concentrations were measured by mobile monitoring during non-heating and heating seasons in Taiyuan. Pollutant concentrations were higher in the nighttime than the daytime, and higher in the heating season than the non-heating season. Daytime/nighttime and full-day LUR models were developed and validated for each pollutant to examine variations in model performance. Adjusted coefficients of determination (adjusted R2) for the LUR models ranged from 0.53-0.87 (PM2.5), 0.53-0.85 (PM10), and 0.33-0.67 (NO2). The performance of the daytime/nighttime LUR models for PM2.5 and PM10 was better than that of the full-day models according to the results of model adjusted R2 and validation R2. Consistent results were confirmed in the non-heating and heating seasons. Effectiveness of developing independent daytime/nighttime models for NO2 to improve performance was limited. Surfaces based on the daytime/nighttime models revealed variations in concentrations and spatial distribution. In conclusion, the independent development of daytime/nighttime LUR models for PM2.5/PM10 has the potential to replace full-day models for better model performance. The modeling strategy is consistent with the residential activity patterns and contributes to achieving reliable exposure predictions for PM2.5 and PM10. Nighttime could be a critical exposure period, due to high pollutant concentrations.
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Affiliation(s)
- Xiangyu Xu
- School of Energy and Environmental Engineering, University of Science and Technology of Beijing, Beijing, 100083, China
| | - Ning Qin
- School of Energy and Environmental Engineering, University of Science and Technology of Beijing, Beijing, 100083, China
| | - Zhenchun Yang
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, London, United Kingdom
| | - Yunwei Liu
- School of Energy and Environmental Engineering, University of Science and Technology of Beijing, Beijing, 100083, China
| | - Suzhen Cao
- School of Energy and Environmental Engineering, University of Science and Technology of Beijing, Beijing, 100083, China
| | - Bin Zou
- School of Geosciences and Info-Physics, Central South University, Changsha, Hunan, 410083, China
| | - Lan Jin
- Department of Surgery, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Yawei Zhang
- Department of Surgery, Yale School of Medicine, New Haven, CT, 06520, USA; Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, 06510, USA
| | - Xiaoli Duan
- School of Energy and Environmental Engineering, University of Science and Technology of Beijing, Beijing, 100083, China.
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Assessment of Remote Sensing Data to Model PM10 Estimation in Cities with a Low Number of Air Quality Stations: A Case of Study in Quito, Ecuador. ENVIRONMENTS 2019. [DOI: 10.3390/environments6070085] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The monitoring of air pollutant concentration within cities is crucial for environment management and public health policies in order to promote sustainable cities. In this study, we present an approach to estimate the concentration of particulate matter of less than 10 µm diameter (PM10) using an empirical land use regression (LUR) model and considering different remote sensing data as the input. The study area is Quito, the capital of Ecuador, and the data were collected between 2013 and 2017. The model predictors are the surface reflectance bands (visible and infrared) of Landsat-7 ETM+, Landsat-8 OLI/TIRS, and Aqua-Terra/MODIS sensors and some environmental indexes (normalized difference vegetation index—NDVI; normalized difference soil index—NDSI, soil-adjusted vegetation index—SAVI; normalized difference water index—NDWI; and land surface temperature (LST)). The dependent variable is PM10 ground measurements. Furthermore, this study also aims to compare three different sources of remote sensing data (Landsat-7 ETM+, Landsat-8 OLI, and Aqua-Terra/MODIS) to estimate the PM10 concentration, and three different predictive techniques (stepwise regression, partial least square regression, and artificial neuronal network (ANN)) to build the model. The models obtained are able to estimate PM10 in regions where air data acquisition is limited or even does not exist. The best model is the one built with an ANN, where the coefficient of determination (R2 = 0.68) is the highest and the root-mean-square error (RMSE = 6.22) is the lowest among all the models. Thus, the selected model allows the generation of PM10 concentration maps from public remote sensing data, constituting an alternative over other techniques to estimate pollutants, especially when few air quality ground stations are available.
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Pollutant composition modification of the effect of air pollution on progression of coronary artery calcium: the Multi-Ethnic Study of Atherosclerosis. Environ Epidemiol 2018; 2. [PMID: 30854505 PMCID: PMC6402342 DOI: 10.1097/ee9.0000000000000024] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Background: Differences in traffic-related air pollution (TRAP) composition may cause heterogeneity in associations between air pollution exposure and cardiovascular health outcomes. Clustering multipollutant measurements allows investigation of effect modification by TRAP profiles. Methods: We measured TRAP components with fixed-site and on-road instruments for two 2-week periods in Baltimore, Maryland. We created representative TRAP profiles for cold and warm seasons using predictive k-means clustering. We predicted cluster membership for 1005 participants in the Multi-Ethnic Study of Atherosclerosis and Air Pollution with follow-up between 2000 and 2012. We estimated cluster-specific relationships between coronary artery calcification (CAC) progression and long-term exposure to fine particulate matter (PM2.5) and oxides of nitrogen (NOX). Results: We identified two clusters in the cold season, notable for higher ratios of gases and ultrafine particles, respectively. A 5-μg/m3 difference in PM2.5 was associated with 17.0 (95% confidence interval [CI] = 7.2, 26.7) and 42.6 (95% CI = 25.7, 59.4) Agatston units/year CAC progression among participants in clusters 1 and 2, respectively (effect modification P = 0.006). A 40 ppb difference in NOX was associated with 22.2 (95% CI = 7.7, 36.7) and 41.9 (95% CI = 23.7, 60.2) Agatston units/year CAC progression in clusters 1 and 2, respectively (P = 0.08). Similar trends occurred using clusters identified from warm season measurements. Clusters correlated highly with baseline pollution level. Conclusions: Clustering TRAP measurements identified spatial differences in composition. We found evidence of greater CAC progression rates per unit PM2.5 exposures among people living in areas characterized by high ratios of ultrafine particle counts relative to NOX concentrations.
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Alexeeff SE, Roy A, Shan J, Liu X, Messier K, Apte JS, Portier C, Sidney S, Van Den Eeden SK. High-resolution mapping of traffic related air pollution with Google street view cars and incidence of cardiovascular events within neighborhoods in Oakland, CA. Environ Health 2018; 17:38. [PMID: 29759065 PMCID: PMC5952592 DOI: 10.1186/s12940-018-0382-1] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Accepted: 03/29/2018] [Indexed: 05/21/2023]
Abstract
BACKGROUND Some studies have linked long-term exposure to traffic related air pollutants (TRAP) with adverse cardiovascular health outcomes; however, previous studies have not linked highly variable concentrations of TRAP measured at street-level within neighborhoods to cardiovascular health outcomes. METHODS Long-term pollutant concentrations for nitrogen dioxide [NO2], nitric oxide [NO], and black carbon [BC] were obtained by street-level mobile monitoring on 30 m road segments and linked to residential addresses of 41,869 adults living in Oakland during 2010 to 2015. We fit Cox proportional hazard models to estimate the relationship between air pollution exposures and time to first cardiovascular event. Secondary analyses examined effect modification by diabetes and age. RESULTS Long-term pollutant concentrations [mean, (standard deviation; SD)] for NO2, NO and BC were 9.9 ppb (SD 3.8), 4.9 ppb (SD 3.8), and 0.36 μg/m3 (0.17) respectively. A one SD increase in NO2, NO and BC, was associated with a change in risk of a cardiovascular event of 3% (95% confidence interval [CI] -6% to 12%), 3% (95% CI -5% to 12%), and - 1% (95% CI -8% to 7%), respectively. Among the elderly (≥65 yrs), we found an increased risk of a cardiovascular event of 12% for NO2 (95% CI: 2%, 24%), 12% for NO (95% CI: 3%, 22%), and 7% for BC (95% CI: -3%, 17%) per one SD increase. We found no effect modification by diabetes. CONCLUSIONS Street-level differences in long-term exposure to TRAP were associated with higher risk of cardiovascular events among the elderly, indicating that within-neighborhood differences in TRAP are important to cardiovascular health. Associations among the general population were consistent with results found in previous studies, though not statistically significant.
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Affiliation(s)
- Stacey E. Alexeeff
- Division of Research, Kaiser Permanente Northern California, 2000 Broadway, Oakland, CA 94612 USA
| | - Ananya Roy
- Environmental Defense Fund, New York, NY USA
| | - Jun Shan
- Division of Research, Kaiser Permanente Northern California, 2000 Broadway, Oakland, CA 94612 USA
| | - Xi Liu
- Division of Research, Kaiser Permanente Northern California, 2000 Broadway, Oakland, CA 94612 USA
| | - Kyle Messier
- Environmental Defense Fund, New York, NY USA
- Dept. of Civil, Architectural and Environmental Engineering, University of Texas at Austin, Austin, TX USA
| | - Joshua S. Apte
- Dept. of Civil, Architectural and Environmental Engineering, University of Texas at Austin, Austin, TX USA
| | | | - Stephen Sidney
- Division of Research, Kaiser Permanente Northern California, 2000 Broadway, Oakland, CA 94612 USA
| | - Stephen K. Van Den Eeden
- Division of Research, Kaiser Permanente Northern California, 2000 Broadway, Oakland, CA 94612 USA
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