Search

Although studies have increasingly linked air pollution to specific health outcomes, less well understood is how public perceptions of air quality respond to changing pollutant levels. The growing availability of air pollution measurements and the proliferation of social media provide an opportunity to gauge public discussion of air quality conditions. In this paper, we consider particulate matter (PM) measurements from four Chinese megacities (Beijing, Shanghai, Guangzhou, and Chengdu) together with 112 million posts on Weibo (a popular Chinese microblogging system) from corresponding days in 2011–2013 to identify terms whose frequency was most correlated with PM levels. These correlations are used to construct an Air Discussion Index (ADI) for estimating daily PM based on the content of Weibo posts. In Beijing, the Chinese city with the most PM as measured by U.S. Embassy monitor stations, we found a strong correlation (R = 0.88) between the ADI and measured PM. In other Chinese cities with lower pollution levels, the correlation was weaker. Nonetheless, our results show that social media may be a useful proxy measurement for pollution, particularly when traditional measurement stations are unavailable, censored or misreported. [ABSTRACT FROM AUTHOR]

Today, China is facing a very serious issue of Air Pollution due to its dreadful impact on the human health as well as the environment. The urban cities in China are the most affected due to their rapid industrial and economic growth. Therefore, it is of extreme importance to come up with new, better and more reliable forecasting models to accurately predict the air quality. This paper selected Beijing, Tianjin and Shijiazhuang as three cities from the Jingjinji Region for the study to come up with a new model of collaborative forecasting using Support Vector Regression (SVR) for Urban Air Quality Index (AQI) prediction in China. The present study is aimed to improve the forecasting results by minimizing the prediction error of present machine learning algorithms by taking into account multiple city multi-dimensional air quality information and weather conditions as input. The results show that there is a decrease in MAPE in case of multiple city multi-dimensional regression when there is a strong interaction and correlation of the air quality characteristic attributes with AQI. Also, the geographical location is found to play a significant role in Beijing, Tianjin and Shijiazhuang AQI prediction. [ABSTRACT FROM AUTHOR]

Currently, Haze is one of the greatest environmental problems with serious impacts on human health in China, especially in capital region (Beijing-Tianjin-Hebei region). To alleviate this problem, the Chinese government introduced a National Air Pollution Control Action Plan (NAPCAP) with air pollutants reduction targets by 2017. However, there is doubt whether these targets can be achieved once the plan is implemented. In this work, the effectiveness of NAPCAP is analyzed by developing models of the statistical relationship between PM2.5 concentrations and air pollutant emissions (SO2, NOx, smoke and dust), while taking into account wind and neighboring transfer impacts. The model can also identify ways of calculating the intended emission levels in the Beijing–Tianjin–Hebei area. The results indicate that haze concentration control targets will not be attained by following the NAPCAP, and that the amount of progress needed to meet the targets is unrealistic. A more appropriate approach to reducing air emissions is proposed, which addresses joint regional efforts. [ABSTRACT FROM AUTHOR]

Air and soil pollution from mining activities has been considered as a critical issue to the health of living organisms. However, few efforts have been made in distinguishing the main pathway of organism genetic damage by heavy metals related to mining activities. Therefore, we investigated the genetic damage of Leymus chinensis leaf cells, the air particulate matter (PM) contents, and concentrations of the main heavy metals (Pb, Cd, Cr, Hg) in soil and foliar dust samples collected from seven experiment points at the core mining area and one control point 20 kilometers away from the core mining area in Inner Mongolia in 2013. Comet assay was used to test the genetic damage of the Leymus chinensis leaf cells; the Tail DNA% and Tail Moment were used to characterize the genetic damage degree of the plant cells. The comet assay results showed that the cell genetic damage ratio was up to 77.0% in experiment points but was only 35.0% in control point. The control point also had the slight Tail DNA% and Tail Moment values than other experiment groups. The cell damage degree of the control group was 0.935 and experiment groups were 1.299–1.815. The geo-accumulation index and comperehensive pollution index(CPI) were used to characterize heavy metal pollution in foliar dust samples, and single factor pollution index and CPI were used to characterize the heavy metal pollution in soil samples. The CPIfoliar dust of control group was 0.36 and experiment groups were 1.45–2.57; the CPIsoil of control group was 0.04 and experiment groups were 0.07–0.12. The results of correlation analyze showed that Air Quality Index (AQI) -CPIfoliar dust(r = 0.955**)>Damage degree-CPIfoliar dust(r = 0.923**)>Damage degree-AQI(r = 0.908**)>Damage degree-CPIsoil (r = 0.824*). The present research proved that mining activity had a high level of positive correlation with organism genetic damage caused by heavy metals through comparing with the control point; soil and atmosphere were both the important action pathway for heavy metal induced genetic damage in mining area. Furthermore, heavy metal contents in foliar dust showed a higher positive correlation with genetic damage than when compared with soil. This means the heavy metal contents that L.chinensis absorbed through respiration from the atmosphere could make more serious genetic damage than when absorbed by root systems from soil in the mining area. This study can provide theoretical support for research on plant genetic damage mechanisms and exposure pathways induced by environmental pollution. [ABSTRACT FROM AUTHOR]

Background and purpose: The data concerning the association between environmental pollution and epilepsy attacks are limited. The aim of this study was to explore the association between acute air pollution exposure and epilepsy attack. Methods: A hospital record-based study was carried out in Xi’an, a heavily-polluted metropolis in China. Daily baseline data were obtained. Time-series Poisson regression models were applied to analyze the association between air pollution and epilepsy. Results: A 10 μg/m3 increase of NO2, SO2, and O3 concentrations corresponded to 3.17% (95%Cl: 1.41%, 4.93%), 3.55% (95%Cl: 1.93%, 5.18%), and -0.84% (95%Cl: -1.58%, 0.09%) increase in outpatient-visits for epilepsy on the concurrent days, which were significantly influenced by sex and age. The effects of NO2 and SO2 would be stronger when adjusted for PM2.5. As for O3, a -1.14% (95%Cl: -1.90%, -0.39%) decrease was evidenced when adjusted for NO2. The lag models showed that the most significant effects were evidenced on concurrent days. Conclusions: We discovered previously undocumented relationships between short-term air pollution exposure and epilepsy: while NO2 and SO2 were positively associated with outpatient-visits of epilepsy, O3 might be associated with reduced risk. [ABSTRACT FROM AUTHOR]