浙江省大气PM_(2.5)时空分布及相关因子分析  

Spatiotemporal distribution and correlation factors of PM concentrations in Zhejiang Province

作　　者：陈兵红[1] 靳全锋 柴红玲[1] 郭福涛[2] CHEN Binghong;JIN Quanfeng;CHAI Hongling;GUO Futao(Lishui Vocational and Technical College,Lishui 323000;Forestry College,Fujian Agriculture and Forestry University,Fuzhou 350002)

机构地区：[1]丽水职业技术学院,丽水323000 [2]福建农林大学林学院,福州350002

出　　处：《环境科学学报》2021年第3期817-829,共13页Acta Scientiae Circumstantiae

基　　金：国家自然科学基金(No.31770697);2017年浙江省访问工程师项目(No.FG2017240);浙江省教育厅一般项目(No.Y201840513)。

摘　　要：该研究以浙江省2014—2019年PM_(2.5)浓度数据为研究对象,应用多元线性回归和随机森林方法结合气象、植被、地形、经济、人口和基础设施等因子进行分析.研究结果表明PM_(2.5)浓度时空分布不均匀,时间上季节变化差异显著,总体呈冬季>春季>秋季>夏季分布规律,每年呈下降趋势;空间上呈西北多东南少的分布特征.多元线性回归和随机森林模型显示日最低地表气温(MI-GST)、日最低气压(MI-PRS)、日蒸发量(EVP)、日最小相对湿度(MI-RHU)、月植被覆盖度(FVC)、日降水量(PRE)、日极大风速(MM-WIN)、日平均相对湿度(AV-RHU)、铁路密度(Railway)、日最大风速(MA-WIN)、日照时长(SSD)、海拔(DEM)、日平均风速(AV-WIN)和河流密度(River)等15个因子对PM_(2.5)浓度影响显著;随机森林模型均方根误差(RMSE)、均方绝对百分比误差(MAPE)和变异解释量(R2)分别为0.133、17.83%和0.834,明显优于多元线性回归(0.278、40.48%和0.575),表明随机森林更适合浙江省PM_(2.5)浓度估测,该研究揭示PM_(2.5)时空分布及相关因子分析,为限制空气污染提供有效策略.The multiple linear regression and random forest methods, which combine factors including meteorology, vegetation, terrain, economy, population and infrastructure, were applied to analyze the data of PM_(2.5) concentrations in the Zhejiang region from 2014 to 2019. Results show that the spatial and temporal distribution of PM_(2.5) concentrations varied, with a significant seasonal order of winter > spring > autumn > summer, and the trend of annual PM_(2.5) concentrations gradually decreased over the study period. The spatial concentration distribution of PM_(2.5) in northwest part of the province is much higher in the northeast part of the province. Results of the multiple linear regression and random forest models show that PM_(2.5) concentrations were significantly impacted by a variety of 15 factors, such as the daily minimum surface temperature(MI-GST), daily minimum pressure(MI-PRS), daily evaporation(EVP), daily minimum relative humidity(MI-RHU), monthly vegetation coverage(FVC), daily precipitation(PRE), daily maximum wind speed(MM-WIN), daily average relative humidity(AV-RHU), railway density(Railway), daily maximum wind speed(MA-WIN), sunshine duration(SSD), digital elevation model(DEM), wind speed(AV-WIN) and river density(River). The root mean square error(RMSE), absolute mean square error(MAPE) and variance interpretation(R2) of the random forest model were 0.133, 17.83% and 0.834, respectively, which were substantially better than the multiple linear regression analysis of 0.278, 40.48% and 0.575. This indicates that the random forest model is better for the estimation of Zhejiang data of PM_(2.5) concentrations. Overall this study characterized the spatiotemporal distribution and correlation factors for PM_(2.5) concentrations, which can provide important information on which to base an effective strategy for controlling air pollution.

关 键 词：浙江省 PM_(2.5) 时空分布 相关因子 随机森林模型 多元线性回归 

分 类 号：X513[环境科学与工程—环境工程]