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Zeider K, Manjón I, Betterton EA, Sáez AE, Sorooshian A, Ramírez-Andreotta MD. Backyard aerosol pollution monitors: foliar surfaces, dust enrichment, and factors influencing foliar retention. ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:1200. [PMID: 37700111 PMCID: PMC10636967 DOI: 10.1007/s10661-023-11752-2] [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: 01/22/2023] [Accepted: 08/19/2023] [Indexed: 09/14/2023]
Abstract
Air pollution is one of the leading causes of death from noncommunicable diseases globally, and in Arizona, both mining activities and abandoned agriculture can generate erodible dust. This dust is transported via wind and can carry high amounts of toxic pollutants. Industry-adjacent communities, or "fenceline communities," are generally closer to the pollution sources and are disproportionally impacted by pollution, or in this case, dust. The dust transported from the mine settles into nearby rivers, gardens, and homes, and increases the concentrations of elements beyond their naturally occurring amounts (i.e., enriched). This study was built upon previous community science work in which plant leaves were observed to collect similar concentrations to an accepted dust collection method and illustrated promise for their use as low-cost air quality monitors in these communities. This work investigated the concentration of Na, Mg, Al, K, Ca, Mn, Co, Cu, Zn, Mo, and Ba in dust from the leaves of community-collected backyard and garden plants (foliar dust), as well as if certain variables affected collection efficacy. This assessment evaluated (1) foliar concentration versus surface area for 11 elements, (2) enrichment factor (EF) values and ratios, (3) comparisons of foliar, garden, and yard samples to US Geological Survey data, and (4) what variable significantly affected dust collection efficacy. The EF results indicate that many of the samples were enriched (anthropogenically contaminated) and that the foliar samples were generally more contaminated than the yard and garden soil samples. Leaf surface area was the most influential factor for leaf collection efficiency (p < 0.05) compared to plant family or sampling location. Further studies are needed that standardize the plant species and age and include multiple replicates of the same plant species across partnering communities. This study has demonstrated that foliar dust is enriched in the participating partnering communities and that plant leaf samples can serve as backyard aerosol pollution monitors. Therefore, foliar dust is a viable indicator of outdoor settled dust and aerosol contamination and this is an adoptable monitoring technique for "fenceline communities."
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Affiliation(s)
- Kira Zeider
- Department of Chemical and Environmental Engineering, University of Arizona, Tucson, AZ, USA
| | - Iliana Manjón
- Department of Environmental Science, University of Arizona, Tucson, AZ, USA
| | - Eric A Betterton
- Department of Hydrology and Atmospheric Sciences, University of Arizona, 1177 E Fourth Street, Rm. 429, Tucson, AZ, 85721, USA
| | - A Eduardo Sáez
- Department of Chemical and Environmental Engineering, University of Arizona, Tucson, AZ, USA
| | - Armin Sorooshian
- Department of Chemical and Environmental Engineering, University of Arizona, Tucson, AZ, USA
- Department of Hydrology and Atmospheric Sciences, University of Arizona, 1177 E Fourth Street, Rm. 429, Tucson, AZ, 85721, USA
| | - Mónica D Ramírez-Andreotta
- Department of Environmental Science, University of Arizona, Tucson, AZ, USA.
- Mel and Enid Zuckerman College of Public Health's Division of Community, Environment & Policy, University of Arizona, Tucson, AZ, USA.
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Wang Y, Wang F, Min R, Song G, Song H, Zhai S, Xia H, Zhang H, Ru X. Contribution of local and surrounding anthropogenic emissions to a particulate matter pollution episode in Zhengzhou, Henan, China. Sci Rep 2023; 13:8771. [PMID: 37253757 DOI: 10.1038/s41598-023-35399-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 05/17/2023] [Indexed: 06/01/2023] Open
Abstract
In this study, we simulated the spatial and temporal processes of a particulate matter (PM) pollution episode from December 10-29, 2019, in Zhengzhou, the provincial capital of Henan, China, which has a large population and severe PM pollution. As winter is the high incidence period of particulate pollution, winter statistical data were selected from the pollutant observation stations in the study area. During this period, the highest concentrations of PM2.5 (atmospheric PM with a diameter of less than 2.5 µm) and PM10 (atmospheric PM with a diameter of less than 10 µm) peaked at 283 μg m-3 and 316 μg m-3, respectively. The contribution rates of local and surrounding regional emissions within Henan (emissions from the regions to the south, northwest, and northeast of Zhengzhou) to PM concentrations in Zhengzhou were quantitatively analyzed based on the regional Weather Research and Forecasting model coupled with Chemistry (WRF/Chem). Model evaluation showed that the WRF/Chem can accurately simulate the spatial and temporal variations in the PM concentrations in Zhengzhou. We found that the anthropogenic emissions south of Zhengzhou were the main causes of high PM concentrations during the studied episode, with contribution rates of 14.39% and 16.34% to PM2.5 and PM10, respectively. The contributions of anthropogenic emissions from Zhengzhou to the PM2.5 and PM10 concentrations in Zhengzhou were 7.94% and 7.29%, respectively. The contributions of anthropogenic emissions from the area northeast of Zhengzhou to the PM2.5 and PM10 concentrations in Zhengzhou were 7.42% and 7.18%, respectively. These two areas had similar contributions to PM pollution in Zhengzhou. The area northeast of Zhengzhou had the lowest contributions to the PM2.5 and PM10 concentrations in Zhengzhou (5.96% and 5.40%, respectively).
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Affiliation(s)
- Yaobin Wang
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng, 475004, Henan, China
- Institute of Urban Big Data, College of Geography and Environmental Science, Henan University, Kaifeng, 475004, Henan, China
| | - Feng Wang
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng, 475004, Henan, China
- Institute of Urban Big Data, College of Geography and Environmental Science, Henan University, Kaifeng, 475004, Henan, China
| | - Ruiqi Min
- Institute of Urban Big Data, College of Geography and Environmental Science, Henan University, Kaifeng, 475004, Henan, China
| | - Genxin Song
- Institute of Urban Big Data, College of Geography and Environmental Science, Henan University, Kaifeng, 475004, Henan, China.
| | - Hongquan Song
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng, 475004, Henan, China.
- Henan Key Laboratory of Integrated Air Pollution Control and Ecological Security, Henan University, Kaifeng, 475004, Henan, China.
| | - Shiyan Zhai
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng, 475004, Henan, China
- Institute of Urban Big Data, College of Geography and Environmental Science, Henan University, Kaifeng, 475004, Henan, China
| | - Haoming Xia
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng, 475004, Henan, China
| | - Haopeng Zhang
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng, 475004, Henan, China
- Institute of Urban Big Data, College of Geography and Environmental Science, Henan University, Kaifeng, 475004, Henan, China
| | - Xutong Ru
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng, 475004, Henan, China
- Institute of Urban Big Data, College of Geography and Environmental Science, Henan University, Kaifeng, 475004, Henan, China
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Deary ME, Griffiths SD. A novel approach to the development of 1-hour threshold concentrations for exposure to particulate matter during episodic air pollution events. JOURNAL OF HAZARDOUS MATERIALS 2021; 418:126334. [PMID: 34329015 DOI: 10.1016/j.jhazmat.2021.126334] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 05/22/2021] [Accepted: 06/03/2021] [Indexed: 06/13/2023]
Abstract
Episodic air pollution events that occur because of wildfires, dust storms and industrial incidents can expose populations to particulate matter (PM) concentrations in the thousands of µg m-3. Such events have increased in frequency and duration over recent years, with this trend predicted to continue in the short to medium term because of climate warming. The human health cost of episodic PM events can be significant, and inflammatory responses are measurable even after only a few hours of exposure. Consequently, advice for the protection of public health should be available as quickly as possible, yet the shortest averaging period for which PM exposure guideline values (GVs) are available is 24-h. To address this problem, we have developed a novel approach, based on Receiver Operating Characteristic (ROC) statistical analysis, that derives 1-h threshold concentrations that have a probabilistic relationship with 24-h GVs. The ROC analysis was carried out on PM10 and PM2.5 monitoring data from across the US for the period 2014-2019. Validation of the model against US Air Quality Index (AQI) 24-h breakpoint concentrations for PM showed that the maximum-observed 1-h PM concentration in any rolling 24-h averaging period is an excellent predictor of exceedances of 24-h GVs.
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Affiliation(s)
- Michael E Deary
- Faculty of Engineering and Environment, Department of Geography and Environmental Sciences, University of Northumbria, Ellison Building, Newcastle upon Tyne NE1 8ST, UK.
| | - Simon D Griffiths
- Faculty of Engineering and Environment, Department of Geography and Environmental Sciences, University of Northumbria, Ellison Building, Newcastle upon Tyne NE1 8ST, UK
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Numerical Simulation of Tehran Dust Storm on 2 June 2014: A Case Study of Agricultural Abandoned Lands as Emission Sources. ATMOSPHERE 2021. [DOI: 10.3390/atmos12081054] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
On 2 June 2014, at about 13 UTC, a dust storm arrived in Tehran as a severe hazard that caused injures, deaths, failures in power supply, and traffic disruption. Such an extreme event is not considered as common for the Tehran area, which has raised the question of the dust storm’s origin and the need for increasing citizens’ preparedness during such events. The analysis of the observational data and numerical simulations using coupled dust-atmospheric models showed that intensive convective activity occurred over the south and southwest of Tehran, which produced cold downdrafts and, consequently, high-velocity surface winds. Different dust source masks were used as an input for model hindcasts of the event (forecasts of the past event) to show the capability of the numerical models to perform high-quality forecasts in such events and to expand the knowledge on the storm’s formation and progression. In addition to the proven capability of the models, if engaged in operational use to contribute to the establishment of an early warning system for dust storms, another conclusion appeared as a highlight of this research: abandoned agricultural areas south of Tehran were responsible for over 50% of the airborne dust concentration within the dust storm that surged through Tehran. Such a dust source in the numerical simulation produced a PM10 surface dust concentration of several thousand μm/m3, which classifies it as a dust source hot-spot. The produced evidence indivisibly links issues of land degradation, extreme weather, environmental protection, and health and safety.
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Atmospheric Dynamics and Numerical Simulations of Six Frontal Dust Storms in the Middle East Region. ATMOSPHERE 2021. [DOI: 10.3390/atmos12010125] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
This study analyzes six frontal dust storms in the Middle East during the cold period (October–March), aiming to examine the atmospheric circulation patterns and force dynamics that triggered the fronts and the associated (pre- or post-frontal) dust storms. Cold troughs mostly located over Turkey, Syria and north Iraq played a major role in the front propagation at the surface, while cyclonic conditions and strong winds facilitated the dust storms. The presence of an upper-atmosphere (300 hPa) sub-tropical jet stream traversing from Egypt to Iran constitutes also a dynamic force accompanying the frontal dust storms. Moderate-Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observations are used to monitor the spatial and vertical extent of the dust storms, while model (Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), Copernicus Atmospheric Monitoring Service (CAMS), Regional Climate Model-4 (RegCM4)) simulations are also analyzed. The WRF-Chem outputs were in better agreement with the MODIS observations compared to those of CAMS and RegCM4. The fronts were identified by WRF-Chem simulations via gradients in the potential temperature and sudden changes of wind direction in vertical cross-sections. Overall, the uncertainties in the simulations and the remarkable differences between the model outputs indicate that modelling of dust storms in the Middle East is really challenging due to the complex terrain, incorrect representation of the dust sources and soil/surface characteristics, and uncertainties in simulating the wind speed/direction and meteorological dynamics. Given the potential threat by dust storms, more attention should be directed to the dust model development in this region.
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