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Public transportation automatic fare collection (AFC) systems are able to continuously record large amounts of passenger travel information, providing massive, low-cost data for research on regulations pertaining to public transport. These data can be used not only to analyze characteristics of passengers’ trips but also to evaluate transport policies that promote a travel mode shift and emission reduction. In this study, models combining card, survey, and geographic information systems (GIS) data are established with a research focus on the private driving restriction policies being implemented in an ever-increasing number of cities. The study aims to evaluate the impact of these policies on the travel mode shift, as well as relevant carbon emission reductions. The private driving restriction policy implemented in Beijing is taken as an example. The impact of the restriction policy on the travel mode shift from cars to subways is analyzed through a model based on metro AFC data. The routing paths of these passengers are also analyzed based on the GIS method and on survey data, while associated carbon emission reductions are estimated. The analysis method used in this study can provide reference for the application of big data in evaluating transport policies. Implications: Motor vehicles have become the most prevalent source of emissions and subsequently air pollution within Chinese cities. The evaluation of the effects of driving restriction policies on the travel mode shift and vehicle emissions will be useful for other cities in the future. Transport big data, playing an important support role in estimating the travel mode shift and emission reduction considered, can help related departments to estimate the effects of traffic jam alleviation and environment improvement before the implementation of these restriction policies and provide a reference for relevant decisions. [ABSTRACT FROM PUBLISHER]

To identify the characteristics of air pollutants and factors attributing to the formation of haze in Wuhan, this study analyzed the hourly observations of air pollutants (PM2.5, PM10, NO2, SO2, O3, and CO) from March 1, 2013, to February 28, 2014, and used hybrid receptor models for a case study. The results showed that the annual average concentrations for PM2.5, PM10, NO2, SO2, O3, and CO during the whole period were 89.6 μg m−3, 134.9 μg m−3, 54.9 μg m−3, 32.4 μg m−3, 62.3 μg m−3, and 1.1 mg m−3, respectively. The monthly variations revealed that the peak values of PM2.5, PM10, NO2, SO2, and CO occurred in December because of increased local emissions and severe weather conditions, while the lowest values occurred in July mainly due to larger precipitation. The maximum O3concentrations occurred in warm seasons from May to August, which may be partly due to the high temperature and solar radiation. Diurnal analysis showed that hourly PM2.5, PM10, NO2, and CO concentrations had two ascending stages accompanying by the two traffic peaks. However, the O3concentration variations were different with the highest concentration in the afternoon. A case study utilizing hybrid receptor models showed the significant impact of regional transport on the haze formation in Wuhan and revealed that the mainly potential polluted sources were located in the north and south of Wuhan, such as Baoding and Handan in Hebei province, and Changsha in Hunan province.Implications: Wuhan city requires a 5% reduction of the annual mean of PM2.5concentration by the end of 2017. In order to accomplish this goal, Wuhan has adopted some measures to improve its air quality. This work has determined the main pollution sources that affect the formation of haze in Wuhan by transport. We showed that apart from the local emissions, north and south of Wuhan were the potential sources contributing to the high PM2.5concentrations in Wuhan, such as Baoding and Handan in Hebei province, Zhumadian and Jiaozuo in Henan province, and Changsha and Zhuzhou in Hunan province. [ABSTRACT FROM PUBLISHER]

Particulate matter with aerodynamic diameter below 10 μm (PM10) forecasting is difficult because of the uncertainties in describing the emission and meteorological fields. This paper proposed a wavelet-ARMA/ARIMA model to forecast the short-term series of the PM10concentrations. It was evaluated by experiments using a 10-year data set of daily PM10concentrations from 4 stations located in Taiyuan, China. The results indicated the following: (1) PM10concentrations of Taiyuan had a decreasing trend during 2005 to 2012 but increased in 2013. PM10concentrations had an obvious seasonal fluctuation related to coal-fired heating in winter and early spring. (2) Spatial differences among the four stations showed that the PM10concentrations in industrial and heavily trafficked areas were higher than those in residential and suburb areas. (3) Wavelet analysis revealed that the trend variation and the changes of the PM10concentration of Taiyuan were complicated. (4) The proposed wavelet-ARIMA model could be efficiently and successfully applied to the PM10forecasting field. Compared with the traditional ARMA/ARIMA methods, this wavelet-ARMA/ARIMA method could effectively reduce the forecasting error, improve the prediction accuracy, and realize multiple-time-scale prediction. Implications: Wavelet analysis can filter noisy signals and identify the variation trend and the fluctuation of the PM10time-series data. Wavelet decomposition and reconstruction reduce the nonstationarity of the PM10time-series data, and thus improve the accuracy of the prediction. This paper proposed a wavelet-ARMA/ARIMA model to forecast the PM10time series. Compared with the traditional ARMA/ARIMA method, this wavelet-ARMA/ARIMA method could effectively reduce the forecasting error, improve the prediction accuracy, and realize multiple-time-scale prediction. The proposed model could be efficiently and successfully applied to the PM10forecasting field. [ABSTRACT FROM PUBLISHER]

The noncooperative air pollution reduction model (NCRM) that is currently adopted in China to manage air pollution reduction of each individual province has inherent drawbacks. In this paper, we propose a cooperative air pollution reduction game model (CRM) that consists of two parts: (1) an optimization model that calculates the optimal pollution reduction quantity for each participating province to meet the joint pollution reduction goal; and (2) a model that distribute the economic benefit of the cooperation (i.e., pollution reduction cost saving) among the provinces in the cooperation based on the Shapley value method. We applied the CRM to the case of SO2reduction in the Beijing–Tianjin–Hebei region in China. The results, based on the data from 2003–2009, show that cooperation helps lower the overall SO2pollution reduction cost from 4.58% to 11.29%. Distributed across the participating provinces, such a cost saving from interprovincial cooperation brings significant benefits to each local government and stimulates them for further cooperation in pollution reduction. Finally, sensitivity analysis is performed using the year 2009 data to test the parameters’ effects on the pollution reduction cost savings. Implications:China is increasingly facing unprecedented pressure for immediate air pollution control. The current air pollution reduction policy does not allow cooperation and is less efficient. In this paper we developed a cooperative air pollution reduction game model that consists of two parts: (1) an optimization model that calculates the optimal pollution reduction quantity for each participating province to meet the joint pollution reduction goal; and (2) a model that distributes the cooperation gains (i.e., cost reduction) among the provinces in the cooperation based on the Shapley value method. The empirical case shows that such a model can help improve efficiency in air pollution reduction. The result of the model can serve as a reference for Chinese government pollution reduction policy design. [ABSTRACT FROM PUBLISHER]

Air pollution gained special attention with the rapid development in Beijing. In January 2013, Beijing experienced extreme air pollution, which was not well examined. We thus examine the magnitude of air quality in the particular month by applying the air quality index (AQI), which is based on the newly upgraded Chinese environmental standard. Our finding revealed that (1) air quality has distinct spatial heterogeneity and relatively better air quality was observed in the northwest while worse quality happened in the southeast part of the city; (2) the wind speed is the main determinant of air quality in the city—when wind speed is greater than 4 m/sec, air quality can be significantly improved; and (3) urban impervious surface makes a contribution to the severity of air pollution—that is, with an increase in the fraction of impervious surface in a given area, air pollution is more severe. The results from our study demonstrated the severe pollution in Beijing and its meteorological and landscape factors. Also, the results of this work suggest that very strict air quality management should be conducted when wind speed less than 4 m/sec, especially at places with a large fraction of urban impervious surface. Implications:Prevention of air pollution is rare among methods with controls on meteorological and urban landscape conditions. We present research that utilizes the latest air quality index (AQI) to compare air pollution with meteorological and landscape conditions. We found that wind is the major meteorological factor that determines the air quality. For a given wind speed greater than 4 m/sec, the air quality improved significantly. Urban impervious surface also contributes to the severe air pollution: that is, when the fraction of impervious surface increases, there is more severe air pollution. These results suggest that air quality management should be conducted when wind speed is less than 4 m/sec, especially at places with a larger fraction of urban impervious surface. [ABSTRACT FROM PUBLISHER]

Indoor particulate matter samples were collected in 17 homes in an urban area in Alexandria during the summer season. During air measurement in all selected homes, parallel outdoor air samples were taken in the balconies of the domestic residences. It was found that the mean indoor PM2.5and PM10(particulate matter with an aerodynamic diameter ≤2.5 and ≤10 μm, respectively) concentrations were 53.5 ± 15.2 and 77.2 ± 15.1 µg/m3, respectively. The corresponding mean outdoor levels were 66.2 ± 16.5 and 123.8 ± 32.1 µg/m3, respectively. PM2.5concentrations accounted, on average, for 68.8 ± 12.8% of the total PM10concentrations indoors, whereas PM2.5contributed to 53.7 ± 4.9% of the total outdoor PM10concentrations. The median indoor/outdoor mass concentration (I/O) ratios were 0.81 (range: 0.43–1.45) and 0.65 (range: 0.4–1.07) for PM2.5and PM10, respectively. Only four homes were found with I/O ratios above 1, indicating significant contribution from indoor sources. Poor correlation was seen between the indoor PM10and PM2.5levels and the corresponding outdoor concentrations. PM10levels were significantly correlated with PM2.5loadings indoors and outdoors and this might be related to PM10and PM2.5originating from similar particulate matter emission sources. Smoking, cooking using gas stoves, and cleaning were the major indoor sources contributed to elevated indoor levels of PM10and PM2.5. [ABSTRACT FROM AUTHOR]