Trends in PM2.5 emissions, concentrations and apportionments in Detroit and Chicago

Milando, Chad W.; Huang, Lei; Batterman, Stuart

doi:10.1016/j.atmosenv.2016.01.012

Cited by 43 publications

(25 citation statements)

References 46 publications

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“…For PM 2.5 , the local increment is spatially resolved by assigning near road blocks (within 200 m of a major freeway) the full local increment; this accounts for local PM 2.5 emissions not included in the dispersion model (e.g., secondary formation or dust) that are higher in the near-road environment; more distant blocks are assigned half of the increment. This approach is justified by the current emissions inventory, which shows that mobile sources account for approximately 50% of the PM 2.5 emissions in Detroit [ 48 ], and by receptor modeling results that show 15% to 30% of PM 2.5 is due to diesel exhaust and other mobile sources [ 51 ].…”

Section: Methodsmentioning

confidence: 99%

Disease and Health Inequalities Attributable to Air Pollutant Exposure in Detroit, Michigan

Martenies

Milando

Williams

et al. 2017

IJERPH

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The environmental burden of disease is the mortality and morbidity attributable to exposures of air pollution and other stressors. The inequality metrics used in cumulative impact and environmental justice studies can be incorporated into environmental burden studies to better understand the health disparities of ambient air pollutant exposures. This study examines the diseases and health disparities attributable to air pollutants for the Detroit urban area. We apportion this burden to various groups of emission sources and pollutants, and show how the burden is distributed among demographic and socioeconomic subgroups. The analysis uses spatially-resolved estimates of exposures, baseline health rates, age-stratified populations, and demographic characteristics that serve as proxies for increased vulnerability, e.g., race/ethnicity and income. Based on current levels, exposures to fine particulate matter (PM2.5), ozone (O3), sulfur dioxide (SO2), and nitrogen dioxide (NO2) are responsible for more than 10,000 disability-adjusted life years (DALYs) per year, causing an annual monetized health impact of $6.5 billion. This burden is mainly driven by PM2.5 and O3 exposures, which cause 660 premature deaths each year among the 945,000 individuals in the study area. NO2 exposures, largely from traffic, are important for respiratory outcomes among older adults and children with asthma, e.g., 46% of air-pollution related asthma hospitalizations are due to NO2 exposures. Based on quantitative inequality metrics, the greatest inequality of health burdens results from industrial and traffic emissions. These metrics also show disproportionate burdens among Hispanic/Latino populations due to industrial emissions, and among low income populations due to traffic emissions. Attributable health burdens are a function of exposures, susceptibility and vulnerability (e.g., baseline incidence rates), and population density. Because of these dependencies, inequality metrics should be calculated using the attributable health burden when feasible to avoid potentially underestimating inequality. Quantitative health impact and inequality analyses can inform health and environmental justice evaluations, providing important information to decision makers for prioritizing strategies to address exposures at the local level.

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Section: Methodsmentioning

confidence: 99%

Disease and Health Inequalities Attributable to Air Pollutant Exposure in Detroit, Michigan

Martenies

Milando

Williams

et al. 2017

IJERPH

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“…This factor contributed 3.8 µg m −3 (6.3 % of PM 2.5 mass) during the NCCPC-control period and 11.5 µg m −3 (12.3 %) to PM 2.5 mass in the non-control period. Possible sources for the mineral dust include (i) natural dust, which contains crustal Al, Si, and Ti (Milando et al, 2016); (ii) construction dust, which includes Ca ; and (iii) road dust, which is characterized by trafficrelated species, such as Cu, Zn, Br, and EC (Khan et al, 2016b;Zong et al, 2016). Here, the mineral dust factor did not contain any notable contributions from the traffic-related species.…”

Section: Estimates Of Source Contributionsmentioning

confidence: 78%

Impacts of short-term mitigation measures on PM2.5 and radiative effects: a case study at a regional background site near Beijing, China

Wang

Liu

et al. 2019

Atmos. Chem. Phys.

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Measurements at a background site near Beijing showed that pollution controls implemented during the 19th National Congress of the Communist Party of China (NC-CPC) were effective in reducing PM 2.5 . Mass concentrations of PM 2.5 and its major chemical components were 20.6 %-43.1 % lower during the NCCPC-control period compared with a non-control period, and differences were greater on days with stable meteorological conditions. A receptor model showed that PM 2.5 from traffic-related emissions, biomass burning, industrial processes, and mineral dust was 38.5 %-77.8 % lower during the NCCPC-control versus noncontrol period, but differences in PM 2.5 from coal burning were small, and secondary sources were higher during the NCCPC-control period. During one pollution episode in the non-control period, secondary sources dominated, and the WRF-Chem model showed that the Beijing-Tianjin-Hebei (BTH) region contributed 73.6 % of PM 2.5 mass. A second pollution episode was linked to biomass burning, and BTH contributed 46.9 % of PM 2.5 mass. Calculations based on Interagency Monitoring of Protected Visual Environments (IMPROVE) algorithms showed that organic matter was the largest contributor to light extinction during the non-control period whereas NH 4 NO 3 was the main contributor during the NCCPC. The Tropospheric Ultraviolet and Visible radiation model showed that the average direct radiative forcing (DRF) values at the Earth's surface were −14.0 and −19.3 W m −2 during the NCCPC-control and non-control periods, respectively, and the DRF for the individual PM 2.5 components were 22.7 %-46.7 % lower during the NCCPC. The information and dataset from this study will be useful for developing air pollution control strategies in the BTH region and for understanding associated aerosol radiative effects.

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“…This factor contributes 3.8 µg m -3 (6.3% of PM2.5 mass) and 11.2 µg m -3 (12.3%) to PM2.5 mass during the NCCPC control and non-control periods, respectively. The possible sources for causing mineral dust may include (i) natural dust, which contains Al, Si, and Ti (Milando et al, 2016), (ii) construction dust, which includes Ca (Liu et al, 2017), and (iii) road dust, which refers to the traffic-related species, such as Cu, Zn, Br, and EC (Khan et al, 2016b;Zong et al, 2016). Here the mineral dust factor do not contain any notable contributions from the traffic-related species.…”

Section: Estimation Of Source Contributionsmentioning

confidence: 99%

Impacts of short-term mitigation measures on PM2.5 and radiative effects: a case study from a regional background site near Beijing, China

Wang¹,

Liu²,

Li³

et al. 2018

Preprint

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Abstract. An intensive measurement campaign was conducted in a regional background site near Beijing during the 19th National Congress of the Communist Party of China (NCCPC) to investigate the effectiveness of short-term mitigation measures on PM2.5 and aerosol direct radiative forcing (DRF). Average mass concentration of PM2.5 and its major chemical composition are decreased by 20.6&#8211;43.1&#8201;% during the NCCPC control period compared with the non-control period. When considering days with the stable meteorological conditions, larger reduction of PM2.5 is found compared with that for all days. Further, a positive matrix factorization receptor model shows that the mass concentrations of PM2.5 from traffic-related emissions, biomass burning, industry processes, and mineral dust are reduced by 38.5&#8211;77.8&#8201;% during the NCCPC control period compared with the non-control period. However, there is no significant difference in PM2.5 from coal burning between these two periods, and an increasing trend of PM2.5 mass from secondary inorganic aerosol is found during the NCCPC control period. Two pollution episodes were occurred subsequently after the NCCPC control period. One is dominated by secondary inorganic aerosol, and the WRF-Chem model shows that the Beijing-Tianjin-Hebei (BTH) region contributes 73.6&#8201;% of PM2.5 mass; the other is mainly caused by biomass burning, and the BTH region contributes 46.9&#8201;% of PM2.5 mass. Calculations based on a revised IMPROVE method show that organic matter (OM) is the largest contributor to the light extinction coefficient (bext<\\sub>) during the non-control period while NH4NO3 is the dominant contributor during the NCCPC control period. The Tropospheric Ultraviolet and Visible radiation model reveals that the average DRF values at the Earth's surface are &#8722;14.0 and &#8722;19.3&#8201;W&#8201;m-2 during the NCCPC control and non-control periods, respectively, and the reduction ratios of DRF due to the decrease in PM2.5 components vary from 22.7&#8211;46.7&#8201;% during the NCCPC control period. Our study would further provide valuable information and dataset to help controlling the air pollution and alleviating the cooling effects of aerosols at the surface in Beijing.

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Trends in PM2.5 emissions, concentrations and apportionments in Detroit and Chicago

Cited by 43 publications

References 46 publications

Disease and Health Inequalities Attributable to Air Pollutant Exposure in Detroit, Michigan

Disease and Health Inequalities Attributable to Air Pollutant Exposure in Detroit, Michigan

Impacts of short-term mitigation measures on PM<sub>2.5</sub> and radiative effects: a case study at a regional background site near Beijing, China

Impacts of short-term mitigation measures on PM<sub>2.5</sub> and radiative effects: a case study from a regional background site near Beijing, China

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Trends in PM2.5 emissions, concentrations and apportionments in Detroit and Chicago

Cited by 43 publications

References 46 publications

Disease and Health Inequalities Attributable to Air Pollutant Exposure in Detroit, Michigan

Disease and Health Inequalities Attributable to Air Pollutant Exposure in Detroit, Michigan

Impacts of short-term mitigation measures on PM&lt;sub&gt;2.5&lt;/sub&gt; and radiative effects: a case study at a regional background site near Beijing, China

Impacts of short-term mitigation measures on PM&lt;sub&gt;2.5&lt;/sub&gt; and radiative effects: a case study from a regional background site near Beijing, China

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Impacts of short-term mitigation measures on PM<sub>2.5</sub> and radiative effects: a case study at a regional background site near Beijing, China

Impacts of short-term mitigation measures on PM<sub>2.5</sub> and radiative effects: a case study from a regional background site near Beijing, China