山东泗水春季农田地表空气中PM2.5浓度变化及影响因素

在2019年的3月1日 ?5月31日,以PM2.5采样器、自动气象站与土壤温湿度数据采集器,对山东泗水县开垦农田近地表80 cm处空气中PM2.5逐日浓度与环境因素进行了观测,以回归统计方法分析了PM2.5浓度变化与环境因素的关系.结果显示:在3月 ?5月,山东泗水县开垦农田近地表80 cm处空气中PM2.5浓度平均值为160.98μg·m?3、变化范围在16.67～333.33μg·m?3.同时,PM2.5浓度变化与风速呈显著指数函数关系(P<0.05),与0?5 cm土层土壤湿度呈显著负相关和指数函数关系(P<0.05);PM2.5浓度变化与气温呈极显著倒数函数关系(P<0.01).另外,PM2.5浓度变化与风速、气温、相对湿度和0?5 cm土层土壤温湿度因素多元线性回归分析中,风速和土壤湿度影响达到显著水平(P<0.01).结果表明,在春季,山东泗水县农田近地表空气中PM2.5污染受风速和0?5 cm土层土壤湿度等多种因素影响,会对大气环境产生不可忽视的影响.

Changes in fine particulate matter concentration of near-ground air and its influence on environmental factors at farmlands in Sishui during Spring

The present study observed changes in fine particulate matter (PM2.5) concentration and environmental factors of air 80 cm above the ground surface of cultivated farmlands in Sishui County, Shandong Province, China, during spring (March 1st – May 31st, 2019). PM2.5 samplers and automatic weather stations were installed. Soil temperature and moisture data were monitored, and the associations between changes in PM2.5 concentration and environmental factors were assessed using regression analysis. From March to May, the average PM2.5 concentration was 160.98 μg·m?3, ranging between 16.67 and 333.33 μg·m?3. In addition, changes in PM2.5 concentration were exponentially and positively correlated with wind speed (P < 0.05),and exponentially but negatively correlated soil moisture in the 0 – 5 cm layer (P < 0.05). Moreover, changes in PM2.5 concentration were significantly and inversely correlated with ambient temperature (P < 0.01). Furthermore, multiple regression analysis revealed a significant effect of wind speed and soil moisture in the 5 cm layer on PM2.5 concentration (P < 0.01). These results suggest that in spring, PM2.5 pollution of near-ground air at farmlands in Sishui is significantly affected by wind speed and soil moisture in the 0 – 5 cm layer, in addition to other factors that have a non-negligible impact on the atmospheric environment.