基于2015~2017年西南地区成都市、贵阳市、重庆市、昆明市、拉萨市PM2.5浓度的逐日观测数据以及同期的气象要素(气温、气压、风速、相对湿度)观测数据,分析PM2.5浓度、气象要素的时空分布特征以及其之间的关系。结果表明:西南地区PM2.5...基于2015~2017年西南地区成都市、贵阳市、重庆市、昆明市、拉萨市PM2.5浓度的逐日观测数据以及同期的气象要素(气温、气压、风速、相对湿度)观测数据,分析PM2.5浓度、气象要素的时空分布特征以及其之间的关系。结果表明:西南地区PM2.5年平均浓度在63.84 μg∙m−3~73.93 μg∙m−3区间内先增加后降低;季节平均浓度具有鲜明的季节变化特征,冬季高,夏季低;月平均浓度大致呈现减小–增加–减小–增加的位相变化。PM2.5年平均浓度空间分布形式较稳定,成都市始终呈现PM2.5高浓度聚集(年平均浓度分别为101.27 μg∙m−3、100.64 μg∙m−3、84.90 μg∙m−3)。五个城市PM2.5浓度和气温呈显著负相关;成都市PM2.5浓度与气压呈强正相关,重庆市、昆明市呈负相关;昆明市、成都市PM2.5浓度与风速呈负相关;拉萨市、贵阳市PM2.5浓度与相对湿度呈负相关。研究表明,小风,适宜的气温和湿度,低压,适当的降水会使PM2.5的浓度增加。Based on the daily observation data of PM2.5 concentrations in Chengdu, Guiyang, Chongqing, Kunming, and Lhasa in Southwest China from 2015 to 2017, as well as the observation data of meteorological elements (temperature, air pressure, wind speed, and relative humidity) during the same period, the analysis of PM2.5 concentrations and the spatiotemporal distribution characteristics of meteorological elements and their relationship. The results show that the annual average PM2.5 concentration in Southwest China increases and then decreases in the range of 63.84~73.93 μg∙m−3;the seasonal average concentration has distinct seasonal variation characteristics, high in winter and low in summer;the monthly average concentration generally shows a phase change of decreasing-increasing-decreasing-increasing. The spatial distribution of the annual average PM2.5 concentration is relatively stable, and Chengdu always shows high PM2.5 concentration aggregation (the annual average concentrations are 101.27 μg∙m−3, 100.64 μg∙m−3, and 84.90 μg∙m−3, respectively). PM2.5 concentrations and air temperature in five cities were significantly negatively correlated;PM2.5 concentrations in Chengdu were strongly positively correlated with air pressure, Chongqing and Kunming were negatively correlated;PM2.5 concentrations in Kunming and Chengdu were negatively correlated with wind speed;Lhasa and Guiyang PM2.5 concentrations were negatively correlated with relative humidity. Studies have shown that light winds, suitable temperature and humidity, low pressure, and appropriate precipitation will increase the concentration of PM2.5.展开更多
文摘基于2015~2017年西南地区成都市、贵阳市、重庆市、昆明市、拉萨市PM2.5浓度的逐日观测数据以及同期的气象要素(气温、气压、风速、相对湿度)观测数据,分析PM2.5浓度、气象要素的时空分布特征以及其之间的关系。结果表明:西南地区PM2.5年平均浓度在63.84 μg∙m−3~73.93 μg∙m−3区间内先增加后降低;季节平均浓度具有鲜明的季节变化特征,冬季高,夏季低;月平均浓度大致呈现减小–增加–减小–增加的位相变化。PM2.5年平均浓度空间分布形式较稳定,成都市始终呈现PM2.5高浓度聚集(年平均浓度分别为101.27 μg∙m−3、100.64 μg∙m−3、84.90 μg∙m−3)。五个城市PM2.5浓度和气温呈显著负相关;成都市PM2.5浓度与气压呈强正相关,重庆市、昆明市呈负相关;昆明市、成都市PM2.5浓度与风速呈负相关;拉萨市、贵阳市PM2.5浓度与相对湿度呈负相关。研究表明,小风,适宜的气温和湿度,低压,适当的降水会使PM2.5的浓度增加。Based on the daily observation data of PM2.5 concentrations in Chengdu, Guiyang, Chongqing, Kunming, and Lhasa in Southwest China from 2015 to 2017, as well as the observation data of meteorological elements (temperature, air pressure, wind speed, and relative humidity) during the same period, the analysis of PM2.5 concentrations and the spatiotemporal distribution characteristics of meteorological elements and their relationship. The results show that the annual average PM2.5 concentration in Southwest China increases and then decreases in the range of 63.84~73.93 μg∙m−3;the seasonal average concentration has distinct seasonal variation characteristics, high in winter and low in summer;the monthly average concentration generally shows a phase change of decreasing-increasing-decreasing-increasing. The spatial distribution of the annual average PM2.5 concentration is relatively stable, and Chengdu always shows high PM2.5 concentration aggregation (the annual average concentrations are 101.27 μg∙m−3, 100.64 μg∙m−3, and 84.90 μg∙m−3, respectively). PM2.5 concentrations and air temperature in five cities were significantly negatively correlated;PM2.5 concentrations in Chengdu were strongly positively correlated with air pressure, Chongqing and Kunming were negatively correlated;PM2.5 concentrations in Kunming and Chengdu were negatively correlated with wind speed;Lhasa and Guiyang PM2.5 concentrations were negatively correlated with relative humidity. Studies have shown that light winds, suitable temperature and humidity, low pressure, and appropriate precipitation will increase the concentration of PM2.5.
