Spatial and temporal source apportionment of PM 2.5 in Georgia, 2002 to 2013

Zhai, Xinxin; Mulholland, James A.; Russell, Armistead G.; Holmes, Heather A.

doi:10.1016/j.atmosenv.2017.04.039

Cited by 18 publications

(10 citation statements)

References 69 publications

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“…Literatures on PM 2.5 and PM 10 were often conducted independently, and mainly in health effects, spatial distributions, temporal trends, source apportionment, chemical composition, and influential factors analysis [23][24][25][26][27][28][29][30][31]. In terms of the relationship between PM 2.5 and PM 10 , it is proved that the mass concentration of PM 2.5 was highly correlated with PM 10 [32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

“…Only by combining the uncontrolled factors of meteorology with the controllable factors of anthropogenic emissions can PM pollution be fully understood and effective mitigation measures be developed [39]. Considering the impact of meteorology on PM, statistical approaches such as correlation analysis [40], multiregression [23,41], neural networks [42,43], and generalized additive models [44,45] were widely used. In addition, the study areas of PM pollution in China were several PM concentration monitoring sites [46], or some metropolis [47,48], may be a wellknown region, such as Yangtze River Delta, Pearl River Delta [36,49].…”

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confidence: 99%

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PM_2.5/PM₁₀ Ratios in Eight Economic Regions and Their Relationship with Meteorology in China

Zhao

Chen

et al. 2019

Advances in Meteorology

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China is suffering severe ambient air pollution in recent decades and particulate matter (PM) has become the major pollutant, especially for PM2.5 and PM10, which have highly raised scholars and policy-makers’ attention in last few years. The existing research has focused on the characteristics of PM2.5 and PM10, respectively, or analyzed the correlation between the two pollutants, while the ratio of PM2.5 to PM10 has been taken less consideration. In this study, daily mean PM2.5 and PM10 mass concentrations in 31 provincial capitals from 2014 to 2016 were used to present the temporal variations and spatial distribution of PM2.5/PM10 ratios among eight economic regions. And then, statistical method and correlation analysis were adopted to investigate the relationship between the ratios and AQI, the rate of change on the ratios, and the impact of meteorological parameters on the ratios. The results indicated that PM2.5/PM10 ratios showed an increasing trend from northwest to southeast due to different economic development and industrial types. The highest values were observed in winter among all regions, and the ratios on weekends were higher than that of on weekdays in most of the regions. Besides, domestic heating in northern China had a significant contribution to the ratios. Moreover, ratios had less changes, and the rate of change was stable in summer. As for air quality, the higher the ratio, the larger the possibility of high AQI so that the air pollution will be more severe. In terms of meteorological factors, the results demonstrated that relative humidity, precipitation, and pressure were the most important factors and had significantly positive impacts, while sunshine duration, temperature, and wind speed had negative effects on the ratios. The findings could identify the pollution sources among PM10 and be helpful for making regulation locally to reduce emission which considers anthropogenic sources and meteorological diffusion simultaneously.

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

PM_2.5/PM₁₀ Ratios in Eight Economic Regions and Their Relationship with Meteorology in China

Zhao

Chen

et al. 2019

Advances in Meteorology

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“…Despite these challenges, air quality in cities is of particular concern as many source apportionment studies have shown that traffic sources are a significant contributor to pollution [16,[34][35][36][37][38][39][40][41][42]. People living in close proximity to roadways are at risk of high exposures to air pollutants [43], and many urban populations of low SES groups typically live close to roadways [17,44,45].…”

Section: Introductionmentioning

confidence: 99%

Demographic Inequities in Health Outcomes and Air Pollution Exposure in the Atlanta Area and its Relationship to Urban Infrastructure

et al. 2018

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Environmental burdens such as air pollution are inequitably distributed with groups of lower socioeconomic statuses, which tend to comprise of large proportions of racial minorities, typically bearing greater exposure. Such groups have also been shown to present more severe health outcomes which can be related to adverse pollution exposure. Air pollution exposure, especially in urban areas, is usually impacted by the built environment, such as major roadways, which can be a significant source of air pollution. This study aims to examine inequities in prevalence of cardiovascular and respiratory diseases in the Atlanta metropolitan region as they relate to exposure to air pollution and characteristics of the built environment. Census tract level data were obtained from multiple sources to model health outcomes (asthma, chronic obstructive pulmonary disease, coronary heart disease, and stroke), pollution exposure (particulate matter and nitrogen oxides), demographics (ethnicity and proportion of elderly residents), and infrastructure characteristics (tree canopy cover, access to green space, and road intersection density). Conditional autoregressive models were fit to the data to account for spatial autocorrelation among census tracts. The statistical model showed areas with majority African-American populations had significantly higher exposure to both air pollutants and higher prevalence of each disease. When considering univariate associations between pollution and health outcomes, the only significant association existed between nitrogen oxides and COPD being negatively correlated. Greater percent tree canopy cover and green space access were associated with higher prevalence of COPD, CHD, and stroke. Overall, in considering health outcomes in connection with pollution exposure infrastructure and ethnic demographics, demographics remained the most significant explanatory variable.

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“…The source impacts cannot be measured directly. Mobile source impacts generated by the observation-based receptor model CMB are found to agree well with the long-term trends of NEI (Zhai et al 2017). In this method, we used the CMB-estimated mobile source impacts as representative of mobile source impacts.…”

Section: Cmb Source Apportionmentmentioning

confidence: 99%

“…The profiles for light-duty gasoline vehicle and heavy-duty diesel vehicle were generated by Chow et al (2004). More information about the CMB estimates can be found in Zhai et al (2017). Spatial PM 2.5 mobile source impact modeling approach Daily IMSI fields are derived using a single standard deviation for all years and all locations to normalize the indicator species concentrations (see eqs 1-3).…”

Section: Cmb Source Apportionmentmentioning

confidence: 99%

Spatial PM_2.5mobile source impacts using a calibrated indicator method

Zhai

Mulholland

Friberg

et al. 2019

Journal of the Air & Waste Management Association

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Motor vehicles are major sources of fine particulate matter (PM 2.5 ), and the PM 2.5 from mobile vehicles is associated with adverse health effects. Traditional methods for estimating source impacts that employ receptor models are limited by the availability of observational data. To better estimate temporally and spatially resolved mobile source impacts on PM 2.5 , we developed an approach based on a method that uses elemental carbon (EC), carbon monoxide (CO), and nitrogen oxide (NO x ) measurements as an indicator of mobile source impacts. We extended the original integrated mobile source indicator (IMSI) method in three aspects. First, we generated spatially resolved indicators using 24-hr average concentrations of EC, CO, and NO x estimated at 4 km resolution by applying a method developed to fuse chemical transport model (Community Multiscale Air Quality Model [CMAQ]) simulations and observations. Second, we used spatially resolved emissions instead of county-level emissions in the IMSI formulation. Third, we spatially calibrated the unitless indicators to annually-averaged mobile source impacts estimated by the receptor model Chemical Mass Balance (CMB). Daily total mobile source impacts on PM 2.5 , as well as separate gasoline and diesel vehicle impacts, were estimated at 12 km resolution from 2002 to 2008 and 4 km resolution from 2008 to 2010 for Georgia. The total mobile and separate vehicle source impacts compared well with daily CMB results, with high temporal correlation (e.g., R ranges from 0.59 to 0.88 for total mobile sources with 4 km resolution at nine locations). The total mobile source impacts had higher correlation and lower error than the separate gasoline and diesel sources when compared with observation-based CMB estimates. Overall, the enhanced approach provides spatially resolved mobile source impacts that are similar to observation-based estimates and can be used to improve assessment of health effects.Implications: An approach is developed based on an integrated mobile source indicator method to estimate spatiotemporal PM 2.5 mobile source impacts. The approach employs three air pollutant concentration fields that are readily simulated at 4 and 12 km resolutions, and is calibrated using PM 2.5 source apportionment modeling results to generate daily mobile source impacts in the state of Georgia. The estimated source impacts can be used in investigations of traffic pollution and health.

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Spatial and temporal source apportionment of PM 2.5 in Georgia, 2002 to 2013

Cited by 18 publications

References 69 publications

PM_2.5/PM₁₀ Ratios in Eight Economic Regions and Their Relationship with Meteorology in China

PM_2.5/PM₁₀ Ratios in Eight Economic Regions and Their Relationship with Meteorology in China

Demographic Inequities in Health Outcomes and Air Pollution Exposure in the Atlanta Area and its Relationship to Urban Infrastructure

Spatial PM_2.5mobile source impacts using a calibrated indicator method

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Spatial and temporal source apportionment of PM 2.5 in Georgia, 2002 to 2013

Cited by 18 publications

References 69 publications

PM2.5/PM10 Ratios in Eight Economic Regions and Their Relationship with Meteorology in China

PM2.5/PM10 Ratios in Eight Economic Regions and Their Relationship with Meteorology in China

Demographic Inequities in Health Outcomes and Air Pollution Exposure in the Atlanta Area and its Relationship to Urban Infrastructure

Spatial PM2.5mobile source impacts using a calibrated indicator method

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Product

Resources

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PM_2.5/PM₁₀ Ratios in Eight Economic Regions and Their Relationship with Meteorology in China

PM_2.5/PM₁₀ Ratios in Eight Economic Regions and Their Relationship with Meteorology in China

Spatial PM_2.5mobile source impacts using a calibrated indicator method