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This paper examines the effects of interventions to reduce air pollution during two international events on air quality in Beijing and its neighbor cities. Air quality data were gathered from China's Ministry of Environmental Protection, meteorological data from the China Meteorological Administration and economic data from the China Statistical Yearbook. The paper uses fixed-effect panel data models to empirically evaluate air quality improvement in Beijing and other affected cities before, during, and after the 2008 Olympic Games and the 2014 Asia-Pacific Economic Cooperation summit. Results show substantial improvement in air quality in Beijing and neighboring cities during the two events. However, some of the air quality improvement achieved reverted within a year after the games and within a week after the summit. Furthermore, the improvement achieved during the summit completely reverted and air quality deteriorated severely five days after the summit. It is also found that air quality in China, at least in the cities included in this study, gradually improved over the past 15 years or so. The findings suggest that sustainable interventions and incentive-based programs to reduce emissions from industry production and traffic are the key to maintaining the air pollution reduction achieved during the events. [ABSTRACT FROM AUTHOR]

The effectiveness of national policies for air pollution control has been demonstrated, but the relative effectiveness of short-term emission reduction measures in comparison with national policies has not. Here we show that short-term abatement measures during important international events substantially reduced PM2.5 concentrations, but air quality rebounded to pre-event levels after the measures ceased. Long-term adherence to strict emission reduction policies led to successful decreases of 54% in PM2.5 concentrations in Beijing, and 23% in atmospheric nitrogen deposition in China from 2012 to 2020. Incentivized by "blue skies" type campaigns, economic development and reactive nitrogen pollution are quickly decoupled, showing that a combination of inspiring but aggressive short-term measures and effective but durable long-term policies delivers sustainable air quality improvement. However, increased ammonia concentrations, transboundary pollutant flows, and the complexity to achieving reduction targets under climate change scenarios, underscore the need for the synergistic control of multiple pollutants and inter-regional action. In response to the rebound of air pollution during large events in China, the authors propose that a combination of ambitious short-term measures and enduring long-term pollution control policies is a sustainable solution for improving air quality. [ABSTRACT FROM AUTHOR]

With the rapid development of China's industrialization, the air pollution is becoming more and more serious. It is vital for us to predict the air quality for determining the further prevention measures of avoiding the brought disasters. In this paper, we are going to propose an approach of predicting the air quality based on the multiple data features through fusing the multiple machine learning models. The approach takes the meteorological data and air quality data for the past six days as one batch of input (the whole data set is for 46 days) and employs a multi-model fusion to provide an improved 24-hour prediction of PM2.5 pollutant concentration all over Beijing. During the above process, two focal feature groups are composed. The first focal feature group contains the historical meteorological data, while the second group includes the statistical information, the date information and the polynomial variations. Besides the two groups, we complement one million more data items by employing the time sliding means. Among the supplementary data, we select the most critical 500 features with Light Gradient Boosting Machine (LightGBM) model and send the features as the input to Gradient Boosting Decision Tree (GBDT) and LightGBM models. Meanwhile, we screen the most critical 300 features with eXtreme Gradient Boosting (XGBoost) model and send them as the input to the three prediction models. Referring to each of the models, we respectively gain the optimal parameters through grid search methods and then fuse the models' contribution with the linear weighting. The experiments indicate that the proposed approach based on the weighting fusion is better than that provided by a single modeling scheme, and the loss value is 0.4158 under the SMAPE index. • An approach of predicting air quality with fusing the multiple machine learning models. • The machine learning solutions to model PM2.5 pollutant concentration distribution feature. • The time sliding means for developing one million more data items. • The fusion of multiple models under linear weighting strategy. The experiments indicate that the approach based on the weighting fusion is better than that provided by the single modeling scheme, and the loss value is 0.4158 under the SMAPE index. [ABSTRACT FROM AUTHOR]

Air pollution has become an increasingly serious environmental problem in China. Especially in winter, the air pollution in northern China becomes even worse due to winter heating. The "coal to gas" policy, which uses natural gas to replace coal in the heating system in winter, was implemented in Beijing in the year 2013. However, the effects of this policy reform have not been examined. Using a panel dataset of 16 districts in Beijing, this paper employs a first difference model to examine the impact of the "coal to gas" policy on air quality. Strong evidence shows that the "coal to gas" policy has significantly improved the air quality in Beijing. On average, the "coal to gas" policy reduced sulfur dioxide ( SO 2 ), nitrogen dioxide ( NO 2 ), particulate matter smaller than 10 µm (PM10), particulate matter smaller than 2.5 µm (PM2.5) and carbon monoxide (CO) by 12.08%, 4.89%, 13.07%, 11.94% and 11.10% per year, respectively. We find that the "coal to gas" policy is more effective in areas with less energy use efficiency. The finding of this paper suggests that the government should continue to implement the "coal to gas" policy, so as to alleviate the air pollution in Beijing, China. [ABSTRACT FROM AUTHOR]

Air pollution is a major global problem, closely related to economic and social development and ecological environment construction. Air pollution data for most regions of China have a close correlation with time and seasons and are affected by multidimensional factors such as meteorology and air quality. In contrast with classical peaks-over-threshold modeling approaches, we use a deep learning technique and three new dynamic conditional generalized Pareto distribution (DCP) models with weather and air quality factors for fitting the time-dependence of the air pollutant concentration and make statistical inferences about their application in air quality analysis. Specifically, in the proposed three DCP models, a dynamic autoregressive exponential function mechanism is applied for the time-varying scale parameter and tail index of the conditional generalized Pareto distribution, and a sufficiently high threshold is chosen using two threshold selection procedures. The probabilistic properties of the DCP model and the statistical properties of the maximum likelihood estimation (MLE) are investigated, simulating and showing the stability and sensitivity of the MLE estimations. The three proposed models are applied to fit the PM 2.5 time series in Beijing from 2015 to 2021. Real data are used to illustrate the advantages of the DCP, especially compared to the estimation volatility of GARCH and AIC or BIC criteria. The DCP model involving both the mixed weather and air quality factors performs better than the other two models with weather factors or air quality factors alone. Finally, a prediction model based on long short-term memory (LSTM) is used to predict PM 2.5 concentration, achieving ideal results. [ABSTRACT FROM AUTHOR]

The Chinese "coal-to-gas" and "coal-to-electricity" strategies aim at reducing dispersed coal consumption and related air pollution by promoting the use of clean and low-carbon fuels in northern China. Here, we show that on top of meteorological influences, the effective emission mitigation measures achieved an average decrease of fine particulate matter (PM2.5) concentrations of ∼14% in Beijing and surrounding areas (the "2+26" pilot cities) in winter 2017 compared to the same period of 2016, where the dispersed coal control measures contributed ∼60% of the total PM2.5 reductions. However, the localized air quality improvement was accompanied by a contemporaneous ∼15% upsurge of PM2.5 concentrations over large areas in southern China. We find that the pollution transfer that resulted from a shift in emissions was of a high likelihood caused by a natural gas shortage in the south due to the coal-to-gas transition in the north. The overall shortage of natural gas greatly jeopardized the air quality benefits of the coal-to-gas strategy in winter 2017 and reflects structural challenges and potential threats in China's clean-energy transition. [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]