Previous air pollution control strategies didn’t pay enough attention to regional collaboration and the spatial response sensitivities,resulting in limited control effects in China.This study proposed an effective PM...Previous air pollution control strategies didn’t pay enough attention to regional collaboration and the spatial response sensitivities,resulting in limited control effects in China.This study proposed an effective PM_(2.5)and O_(3) control strategy scheme with the integration of Self-Organizing Map(SOM),Genetic Algorithm(GA)and WRF-CAMx,emphasizing regional collaborative control and the strengthening of control in sensitive areas.This scheme embodies the idea of hierarchical management and spatial-temporally differentiated management,with SOM identifying the collaborative subregions,GA providing the optimized subregion-level priority of precursor emission reductions,and WRF-CAMx providing response sensitivities for grid-level priority of precursor emission reductions.With Beijing-Tianjin-Hebei and the surrounding area(BTHSA,“2+26”cities)as the case study area,the optimized strategy required that regions along Taihang Mountains strengthen the emission reductions of all precursors in PM_(2.5)-dominant seasons,and strengthen VOCs reductions but moderate NOx reductions in O_(3)-dominant season.The spatiotemporally differentiated control strategy,without additional emission reduction burdens than the 14th Five-Year Plan proposed,reduced the average annual PM_(2.5)and MDA8 O_(3) concentrations in 28 cities by 3.2%-8.2% and 3.9%-9.7% respectively in comparison with non-differential control strategies,with the most prominent optimization effects occurring in the heavily polluted seasons(6.9%-18.0%for PM_(2.5)and 3.3%-14.2% for MDA8 O_(3),respectively).This study proposed an effective scheme for the collaborative control of PM_(2.5)and O_(3) in BTHSA,and shows important methodological implications for other regions suffering from similar air quality problems.展开更多
利用CAMX-PSAT(Comprehensive Air Quality Model with Extensions-the Particulate Source Apportionment Technology,CAMx-PSAT)颗粒物溯源技术,解析了2016年12月湖南长沙、株洲、湘潭、邵阳、衡阳和永州等6个重点城市PM2.5及其主要...利用CAMX-PSAT(Comprehensive Air Quality Model with Extensions-the Particulate Source Apportionment Technology,CAMx-PSAT)颗粒物溯源技术,解析了2016年12月湖南长沙、株洲、湘潭、邵阳、衡阳和永州等6个重点城市PM2.5及其主要组分的空间来源。源解析结果表明,湖南省周边区域传输影响显著,外来源平均贡献率超过46.7%,PM2.5受外来源影响最大的区域是株洲市,其次是长沙市,最低的为衡阳市。研究表明,实施周边区域联防联控,协同治理,有效降低外来源传输影响,对于秋冬季节改善湖南省内重点城市环境空气质量将起到关键作用。展开更多
关中地区是我国大气污染的重点监测区域,为探究偏东风输送对关中地区冬季PM_(2.5)重污染的影响,重点分析了2018年1月12—18日在偏东风输送影响下关中地区ρ(PM_(2.5))日均值的变化过程;利用WRF和CAMx模式对PM_(2.5)重污染过程进行模拟...关中地区是我国大气污染的重点监测区域,为探究偏东风输送对关中地区冬季PM_(2.5)重污染的影响,重点分析了2018年1月12—18日在偏东风输送影响下关中地区ρ(PM_(2.5))日均值的变化过程;利用WRF和CAMx模式对PM_(2.5)重污染过程进行模拟并讨论其消长原因.结果表明:①冬季关中地区在高压脊和西南槽的控制下,偏东风将污染物输送至关中地区,加之关中地区地形阻滞,致使关中地区的ρ(PM_(2.5))上升.②研究期间,关中地区ρ(PM_(2.5))日均值范围为103~240μg m 3,偏东风输送是导致此次重污染过程的重要原因.重污染的发生还与气象要素的变化有关,其中ρ(PM_(2.5))日均值与气温、相对湿度均呈滞后相关性.在ρ(PM_(2.5))日均值相等的情况下,相对湿度越大,能见度越低;随着ρ(PM_(2.5))日均值和相对湿度的升高,能见度下降的速率逐渐变慢.③根据WRF-CAMx的模拟结果,此次重污染过程中关中地区PM_(2.5)污染输送关系不均衡,宝鸡市和咸阳市均以本地贡献为主,其本地贡献率超过45.00%,而渭南市接收关中地区其他城市及关中地区以外区域污染输送占比为69.82%;位于盆地中东部的咸阳市、西安市和渭南市的ρ(PM_(2.5))月均值均大于关中地区ρ(PM_(2.5))平均值;渭南市、西安市、运城市以及关中地区以外城市是此次关中地区跨市PM_(2.5)污染输送的主要来源.研究显示,偏东风输送是关中地区此次大气重污染过程的重要原因.展开更多
基金supported by the National Natural Science Foundation of China(Nos.51638001,52000005)。
文摘Previous air pollution control strategies didn’t pay enough attention to regional collaboration and the spatial response sensitivities,resulting in limited control effects in China.This study proposed an effective PM_(2.5)and O_(3) control strategy scheme with the integration of Self-Organizing Map(SOM),Genetic Algorithm(GA)and WRF-CAMx,emphasizing regional collaborative control and the strengthening of control in sensitive areas.This scheme embodies the idea of hierarchical management and spatial-temporally differentiated management,with SOM identifying the collaborative subregions,GA providing the optimized subregion-level priority of precursor emission reductions,and WRF-CAMx providing response sensitivities for grid-level priority of precursor emission reductions.With Beijing-Tianjin-Hebei and the surrounding area(BTHSA,“2+26”cities)as the case study area,the optimized strategy required that regions along Taihang Mountains strengthen the emission reductions of all precursors in PM_(2.5)-dominant seasons,and strengthen VOCs reductions but moderate NOx reductions in O_(3)-dominant season.The spatiotemporally differentiated control strategy,without additional emission reduction burdens than the 14th Five-Year Plan proposed,reduced the average annual PM_(2.5)and MDA8 O_(3) concentrations in 28 cities by 3.2%-8.2% and 3.9%-9.7% respectively in comparison with non-differential control strategies,with the most prominent optimization effects occurring in the heavily polluted seasons(6.9%-18.0%for PM_(2.5)and 3.3%-14.2% for MDA8 O_(3),respectively).This study proposed an effective scheme for the collaborative control of PM_(2.5)and O_(3) in BTHSA,and shows important methodological implications for other regions suffering from similar air quality problems.
文摘利用CAMX-PSAT(Comprehensive Air Quality Model with Extensions-the Particulate Source Apportionment Technology,CAMx-PSAT)颗粒物溯源技术,解析了2016年12月湖南长沙、株洲、湘潭、邵阳、衡阳和永州等6个重点城市PM2.5及其主要组分的空间来源。源解析结果表明,湖南省周边区域传输影响显著,外来源平均贡献率超过46.7%,PM2.5受外来源影响最大的区域是株洲市,其次是长沙市,最低的为衡阳市。研究表明,实施周边区域联防联控,协同治理,有效降低外来源传输影响,对于秋冬季节改善湖南省内重点城市环境空气质量将起到关键作用。
文摘关中地区是我国大气污染的重点监测区域,为探究偏东风输送对关中地区冬季PM_(2.5)重污染的影响,重点分析了2018年1月12—18日在偏东风输送影响下关中地区ρ(PM_(2.5))日均值的变化过程;利用WRF和CAMx模式对PM_(2.5)重污染过程进行模拟并讨论其消长原因.结果表明:①冬季关中地区在高压脊和西南槽的控制下,偏东风将污染物输送至关中地区,加之关中地区地形阻滞,致使关中地区的ρ(PM_(2.5))上升.②研究期间,关中地区ρ(PM_(2.5))日均值范围为103~240μg m 3,偏东风输送是导致此次重污染过程的重要原因.重污染的发生还与气象要素的变化有关,其中ρ(PM_(2.5))日均值与气温、相对湿度均呈滞后相关性.在ρ(PM_(2.5))日均值相等的情况下,相对湿度越大,能见度越低;随着ρ(PM_(2.5))日均值和相对湿度的升高,能见度下降的速率逐渐变慢.③根据WRF-CAMx的模拟结果,此次重污染过程中关中地区PM_(2.5)污染输送关系不均衡,宝鸡市和咸阳市均以本地贡献为主,其本地贡献率超过45.00%,而渭南市接收关中地区其他城市及关中地区以外区域污染输送占比为69.82%;位于盆地中东部的咸阳市、西安市和渭南市的ρ(PM_(2.5))月均值均大于关中地区ρ(PM_(2.5))平均值;渭南市、西安市、运城市以及关中地区以外城市是此次关中地区跨市PM_(2.5)污染输送的主要来源.研究显示,偏东风输送是关中地区此次大气重污染过程的重要原因.