scholarly journals Characteristics and Formation Mechanisms of Fine Particulate Nitrate in Typical Urban Areas in China

Atmosphere ◽  
2017 ◽  
Vol 8 (12) ◽  
pp. 62 ◽  
Author(s):  
Xinlei Ge ◽  
Yanan He ◽  
Yele Sun ◽  
Jianzhong Xu ◽  
Junfeng Wang ◽  
...  
Keyword(s):  
Urban Areas ◽  
Formation Mechanisms ◽  
Fine Particulate

scholarly journals An Economic Evaluation of the Health Effects of Reducing Fine Particulate Pollution in Chinese Cities

2018 ◽  
Vol 35 (2) ◽  
pp. 58-84 ◽  
Author(s):  
Yana Jin ◽  
Shiqiu Zhang
Keyword(s):  
Air Quality ◽  
Urban Areas ◽  
Control Process ◽  
Significant Heterogeneity ◽  
Mortality Reduction ◽  
Republic Of China ◽  
Particulate Pollution ◽  
Premature Deaths ◽  
Fine Particulate ◽  
Welfare Implications

Fine particulate pollution (PM2.5) is a leading mortality risk factor in the People's Republic of China (PRC) and many Asian countries. Current studies of PM2.5 mortality have been conducted at the national and provincial levels, or at the grid-based micro level, and report only the exposure index or attributable premature deaths. Little is known about the welfare implications of PM2.5 mortality for urban areas. In this study, we estimate the total cost of PM2.5 mortality, the benefit of its reduction achieved through meeting various air quality targets, and the benefit of mortality reduction achieved through a uniform 10 micrograms per cubic meter decrease in PM2.5 concentration in the urban areas of 300 major cities in the PRC. Significant heterogeneity exists in welfare indicators across rich versus poor and clean versus dirty cities. The results indicate that cities in the PRC should accelerate the fine particulate pollution control process and implement more stringent air quality targets to achieve much greater mortality reduction benefits.

scholarly journals Impacts of East Mediterranean megacity emissions on air quality

2012 ◽  
Vol 12 (14) ◽  
pp. 6335-6355 ◽  
Author(s):  
U. Im ◽  
M. Kanakidou
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Urban Areas ◽  
Surface Ozone ◽  
Fine Particulate Matter ◽  
Biogenic Emissions ◽  
Anthropogenic Emissions ◽  
East Mediterranean ◽  
Fine Particulate ◽  
Surface O3

Abstract. Megacities are large urban agglomerations with intensive anthropogenic emissions that have significant impacts on local and regional air quality. In the present mesoscale modeling study, the impacts of anthropogenic emissions from the Greater Istanbul Area (GIA) and the Greater Athens Area (GAA) on the air quality in GIA, GAA and the entire East Mediterranean are quantified for typical wintertime (December 2008) and summertime (July 2008) conditions. They are compared to those of the regional anthropogenic and biogenic emissions that are also calculated. Finally, the efficiency of potential country-based emissions mitigation in improving air quality is investigated. The results show that relative contributions from both cities to surface ozone (O3) and aerosol levels in the cities' extended areas are generally higher in winter than in summer. Anthropogenic emissions from GIA depress surface O3 in the GIA by ~ 60% in winter and ~ 20% in summer while those from GAA reduce the surface O3 in the GAA by 30% in winter and by 8% in summer. GIA and GAA anthropogenic emissions contribute to the fine particulate matter (PM2.5) levels inside the cities themselves by up to 75% in winter and by 50% (GIA) and ~ 40% (GAA), in summer. GIA anthropogenic emissions have larger impacts on the domain-mean surface O3 (up to 1%) and PM2.5 (4%) levels compared to GAA anthropogenic emissions (<1% for O3 and ≤2% for PM2.5) in both seasons. Impacts of regional anthropogenic emissions on the domain-mean surface pollutant levels (up to 17% for summertime O3 and 52% for wintertime fine particulate matter, PM2.5) are much higher than those from Istanbul and Athens together (~ 1% for O3 and ~ 6% for PM2.5, respectively). Regional biogenic emissions are found to limit the production of secondary inorganic aerosol species in summer up to 13% (non-sea-salt sulfate (nss-SO42−) in rural Athens) due to their impact on oxidant levels while they have negligible impact in winter. Finally, the responses to country-based anthropogenic emission mitigation scenarios inside the studied region show increases in O3 mixing ratios in the urban areas of GIA and GAA, higher in winter (~ 13% for GIA and 2% for GAA) than in summer (~ 7% for GIA and <1% for GAA). On the opposite PM2.5 concentrations decrease by up to 30% in GIA and by 20% in GAA with the highest improvements computed for winter. The emission reduction strategy also leads to domain-wide decreases in most primary pollutants like carbon monoxide (CO) or nitrogen oxides (NOx) for both seasons. The results show the importance of long range transport of pollutants for the air quality in the East Mediterranean. Thus, improvements of air quality in the East Mediterranean require coordinated efforts inside the region and beyond.

Hybrid Model for Prediction of Carbon Monoxide and Fine Particulate Matter Concentrations near a Road Intersection

2015 ◽  
Vol 2503 (1) ◽  
pp. 29-38 ◽  
Author(s):  
Zhanyong Wang ◽  
Hong-Di He ◽  
Feng Lu ◽  
Qing-Chang Lu ◽  
Zhong-Ren Peng
Keyword(s):  
Neural Network ◽  
Carbon Monoxide ◽  
Particulate Matter ◽  
Air Quality ◽  
Hybrid Model ◽  
Urban Areas ◽  
Fine Particulate Matter ◽  
Principal Component ◽  
Fine Particulate ◽  
Proposed Model

Air quality time series near road intersections consist of complex linear and nonlinear patterns and are difficult to forecast. The backpropagation neural network (BPNN) has been applied for air quality forecasting in urban areas, but it has limited accuracy because of the inability to predict extreme events. This study proposed a novel hybrid model called GAWNN that combines a genetic algorithm and a wavelet neural network to improve forecast accuracy. The proposed model was examined through predicting the carbon monoxide (CO) and fine particulate matter (PM2.5) concentrations near a road intersection. Before the predictions, principal component analysis was adopted to generate principal components as input variables to reduce data complexity and collinearity. Then the GAWNN model and the BPNN model were implemented. The comparative results indicated that GAWNN provided more reliable and accurate predictions of CO and PM2.5 concentrations. The results also showed that GAWNN performed better than BPNN did in the capability of forecasting extreme concentrations. Furthermore, the spatial transferability of the GAWNN model was reasonably good despite a degenerated performance caused by the unavoidable difference between the training and test sites. These findings demonstrate the potential of the application of the proposed model to forecast the fine-scale trend of air pollution in the vicinity of a road intersection.

scholarly journals Assessment of Population Exposure to Coarse and Fine Particulate Matter in the Urban Areas of Chennai, India

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Ramachandran Prasannavenkatesh ◽  
Ramachandran Andimuthu ◽  
Palanivelu Kandasamy ◽  
Geetha Rajadurai ◽  
Divya Subash Kumar ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Urban Areas ◽  
Respiratory Health ◽  
Fine Particulate Matter ◽  
Population Data ◽  
Epidemiological Studies ◽  
World Health ◽  
Population Exposure ◽  
Fine Particulates ◽  
Fine Particulate

Research outcomes from the epidemiological studies have found that the course (PM10) and the fine particulate matter (PM2.5) are mainly responsible for various respiratory health effects for humans. The population-weighted exposure assessment is used as a vital decision-making tool to analyze the vulnerable areas where the population is exposed to critical concentrations of pollutants. Systemic sampling was carried out at strategic locations of Chennai to estimate the various concentration levels of particulate pollution during November 2013–January 2014. The concentration of the pollutants was classified based on the World Health Organization interim target (IT) guidelines. Using geospatial information systems the pollution and the high-resolution population data were interpolated to study the extent of the pollutants at the urban scale. The results show that approximately 28% of the population resides in vulnerable locations where the coarse particulate matter exceeds the prescribed standards. Alarmingly, the results of the analysis of fine particulates show that about 94% of the inhabitants live in critical areas where the concentration of the fine particulates exceeds the IT guidelines. Results based on human exposure analysis show the vulnerability is more towards the zones which are surrounded by prominent sources of pollution.

scholarly journals The air pollution tradeoff in India: Saving more lives versus reducing the inequality of exposure

2022 ◽  
Author(s):  
Ashwini Sankar ◽  
Andrew Goodkind ◽  
Jay Coggins
Keyword(s):  
Response Function ◽  
Urban Areas ◽  
Thought Experiment ◽  
Fine Particulate Matter ◽  
Global Public Health ◽  
Fixed Amount ◽  
Premature Deaths ◽  
Fine Particulate ◽  
Unit Reduction ◽  
Pm2.5 Concentration

Abstract Chronic exposure to ambient fine particulate matter (PM2.5) represents one of the largest global public health risks, leading to millions of premature deaths annually. For a country facing high and spatially variable exposures, prioritizing where to reduce PM2.5 concentrations leads to an inherent tradeoff between saving the most lives and reducing inequality of exposure. This tradeoff results from the shape of the concentration-response function between exposure to PM2.5 and mortality, which indicates that the additional lives saved per unit reduction in PM2.5 declines as concentrations increase. We estimate this concentration-response function for urban areas of India, finding that a 10 unit reduction in PM2.5 in already-clean locations will reduce the mortality rate substantially (4.2% for a reduction from 30 to 20 µgm-3), while a 10 unit reduction in the dirtiest locations will reduce mortality only modestly (1.2% for a reduction from 90 to 80 µgm-3). We explore the implications of this PM2.5/mortality relationship by considering a thought experiment. If India had a fixed amount of resources to devote to PM2.5 concentration reductions across urban areas, what is the lives saved/inequality of exposure tradeoff from three different methods of employing those resources? Across our three scenarios—1) which reduces exposures for the dirtiest districts, 2) which reduces exposures everywhere equally, and 3) which reduces exposures to save the most lives—scenario 1 saves 18,000 lives per year while reducing the inequality of exposure by 65%, while scenario 3 saves 126,000 lives per year, but increases inequality by 19%.

Sign in / Sign up

Export Citation Format

Share Document