Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China.
Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China.
Sci Total Environ. 2018 Apr 1;619-620:436-445. doi: 10.1016/j.scitotenv.2017.11.124. Epub 2017 Nov 29.
The frequent occurrence of extreme smog episodes in recent years has begun to present a serious threat to human health. In addition to pollutant emissions and meteorological conditions, fine particulate matter (PM) is also influenced by socioeconomic development. Thus, identifying the potential effects of socioeconomic development on PM variations can provide insights into particulate pollution control. This study applied spatial regression and the geographical detector technique for assessing the directions and strength of association between socioeconomic factors and PM concentrations, using data collected from 945 monitoring stations in 190 Chinese cities in 2014. The results indicated that the annual average PM concentrations is 61±20μg/m, and cites with more than 75μg/m were mainly located in North China, especially in Tianjin and Hebei province. We also identified a marked seasonal variation in concentrations levels, with the highest level in winter due to coal consumption, lower temperatures, and less rainfall than in summer. Monthly variations followed a "U-shaped" pattern, with a down trend from January and an inflection point in September and then an increasing trend from October. The results of spatial regression indicated that population density, industrial structure, industrial soot (dust) emissions, and road density have a significantly positive effect on PM concentrations, with a significantly negative influence exerted only by economic growth. In addition, trade openness and electricity consumption were found to have no significant impact on PM concentrations. Using the geographical detector technique, the strength of association between the five significant drivers and PM concentrations was further analyzed. We found notable differences among the variables, with industrial soot (dust) emissions playing a greater role in the PM concentrations than the other variables. These results will be helpful in understanding the dynamics and the underlying mechanisms at work in PM concentrations in China at the city level, and thereby assisting the Chinese government in employing effective strategies to tackle pollution.
近年来，极端雾霾事件频繁发生，开始对人类健康构成严重威胁。除了污染物排放和气象条件外，细颗粒物（PM）还受社会经济发展的影响。因此，确定社会经济发展对 PM 变化的潜在影响，可以深入了解颗粒物污染控制。本研究应用空间回归和地理探测器技术，评估 2014 年中国 190 个城市的 945 个监测站的社会经济因素与 PM 浓度之间的方向和关联强度。结果表明，年均 PM 浓度为 61±20μg/m，浓度超过 75μg/m 的城市主要位于华北地区，特别是天津和河北省。我们还发现浓度水平存在明显的季节性变化，冬季由于煤炭消耗、较低的温度和较少的降雨量导致浓度最高。每月变化呈“U 型”模式，1 月呈下降趋势，9 月出现拐点，然后从 10 月开始呈上升趋势。空间回归结果表明，人口密度、产业结构、工业烟尘（粉尘）排放和道路密度对 PM 浓度有显著的正影响，只有经济增长对 PM 浓度有显著的负影响。此外，贸易开放度和用电量对 PM 浓度没有显著影响。利用地理探测器技术，进一步分析了五个显著驱动因素与 PM 浓度之间的关联强度。我们发现这些变量之间存在显著差异，工业烟尘（粉尘）排放对 PM 浓度的影响大于其他变量。这些结果将有助于了解中国城市层面 PM 浓度的动态变化和潜在机制，从而为中国政府采取有效策略应对污染提供帮助。
