呼和浩特市轻、重污染区空气颗粒物质量浓度及变化特征
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摘要
目的了解呼和浩特市轻重污染区空气颗粒污染物水平及变化趋势。方法选择呼和浩特市回民区未来城社区设定为重污染区监测点,呼和浩特市赛罕区乌兰察布东路社区设置为轻污染区监测点,分别在两个监测点监测2014—2017每日24 h的空气颗粒物PM_(10)和PM_(2.5)浓度,通过每日颗粒物测量值计算年平均质量浓度,2014—2017年各季度污染物平均质量浓度,从而进行具体分析,运用卡方检验对比分析两监测点历年空气颗粒物质量浓度变化趋势。结果呼和浩特市轻污染区赛罕区不同年份PM_(10)质量浓度差异有统计学意义(H=110.661,P<0.001),2014年最高,2014—2016年逐年降低,2017年有所回升,PM_(2.5)质量浓度差异有统计学意义(H=33.178,P<0.001),2014年平均质量浓度最高,同样2014—2016年逐年降低,2017年有所升高。重污染区回民区不同年份PM_(10)质量浓度差异有统计学意义(H=33.475,P<0.001)2014年最高,2014—2016年逐年降低,2017年相对较高。PM_(2.5)质量浓度差异有统计学意义(H=57.582,P<0.001)2014年最高,2015—2017年呈逐年升高趋势。回民区PM_(10)年平均质量浓度高于赛罕区,差异有统计学意义(Z=-10.644,P<0.001)。回民区PM_(2.5)年平均浓度高于赛罕区,差异有统计学意义(Z=-10.189,P<0.001)。2014—2017年回民区PM_(10)和PM_(2.5)超标天数均多于赛罕区。结论呼和浩特市回民区空气颗粒物PM_(10)和PM_(2.5)年平均质量浓度高于赛罕区,高空气颗粒物质量浓度主要集中在冬季。两地区2014—2017年空气颗粒物PM_(10)和PM_(2.5)质量浓度总体呈下降趋势。
Objectives To assess the concentration of particulate matters in heavy and light pollution areas of Hohhot and their variation tendencies. Methods The Future City Community in Huimin District of Hohhot was selected as the monitoring point of heavy pollution area, and the Wulanchabu East Road Community in Saihan District was selected as the monitoring point of light pollution area. The TH-150 c intelligent medium flow sampler was used at two monitoring points to measure the concentrations of PM_(10) and PM_(2.5) of air particles for 24 h every day from 2014 to 2017. The annual average concentration was calculated by measuring the concentrations of daily particles. At the same time, the quarterly average concentration with the latest year's(2017) data for specific analysis was calculated. Chi-square test was used to compare and analyze the variation trend of air particle concentrations in the two monitoring points over the years. Results The PM_(10) concentration differences in different years in Saihan District, a light pollution area in Hohhot, were statistically significant(H=110.661, P<0.001). The highest was in 2014, decreased year by year from 2014 to 2016, and then picked up in 2017. The PM_(2.5) concentration differences were statistically significant(H=33.178, P<0.001). The highest was in 2014, decreased year by year from 2014 to 2016, and increased in 2017. The difference of PM_(10) concentrations in the heavily polluted Huimin area was statistically significant in different years(H=33.475, P<0.001). It was highest in 2014, declining year by year from 2014 to 2016, and picked up in 2017. The difference in PM_(2.5) concentrations was statistically significant(H=57.582, P<0.001), which was the highest in 2014, and increased year by year from 2015 to 2017. The PM_(10) annual average concentration of Huimin district was higher than that of Saihan District, and the difference was statistically significant(Z=segregation 10.644, P<0.001). The annual average concentration of PM_(2.5) in Huimin district was higher than that in Saihan district, and the difference was statistically significant(Z=way10.189, P<0.001). From 2014 to 2017, the number of days with excessive PM_(10) and PM_(2.5) in Huimin District was higher than that in Saihan. Conclusions The annual average concentration of PM_(10) and PM_(2.5) in Huimin was higher than that in Saihan, and high concentrations of high airborne particulate matter mainly appeared in winter. From 2014 to 2017, the concentrations of air particulate matters PM_(10) and PM_(2.5) in the two regions showed an overall downward trend.
Objectives To assess the concentration of particulate matters in heavy and light pollution areas of Hohhot and their variation tendencies. Methods The Future City Community in Huimin District of Hohhot was selected as the monitoring point of heavy pollution area, and the Wulanchabu East Road Community in Saihan District was selected as the monitoring point of light pollution area. The TH-150 c intelligent medium flow sampler was used at two monitoring points to measure the concentrations of PM_(10) and PM_(2.5) of air particles for 24 h every day from 2014 to 2017. The annual average concentration was calculated by measuring the concentrations of daily particles. At the same time, the quarterly average concentration with the latest year's(2017) data for specific analysis was calculated. Chi-square test was used to compare and analyze the variation trend of air particle concentrations in the two monitoring points over the years. Results The PM_(10) concentration differences in different years in Saihan District, a light pollution area in Hohhot, were statistically significant(H=110.661, P<0.001). The highest was in 2014, decreased year by year from 2014 to 2016, and then picked up in 2017. The PM_(2.5) concentration differences were statistically significant(H=33.178, P<0.001). The highest was in 2014, decreased year by year from 2014 to 2016, and increased in 2017. The difference of PM_(10) concentrations in the heavily polluted Huimin area was statistically significant in different years(H=33.475, P<0.001). It was highest in 2014, declining year by year from 2014 to 2016, and picked up in 2017. The difference in PM_(2.5) concentrations was statistically significant(H=57.582, P<0.001), which was the highest in 2014, and increased year by year from 2015 to 2017. The PM_(10) annual average concentration of Huimin district was higher than that of Saihan District, and the difference was statistically significant(Z=segregation 10.644, P<0.001). The annual average concentration of PM_(2.5) in Huimin district was higher than that in Saihan district, and the difference was statistically significant(Z=way10.189, P<0.001). From 2014 to 2017, the number of days with excessive PM_(10) and PM_(2.5) in Huimin District was higher than that in Saihan. Conclusions The annual average concentration of PM_(10) and PM_(2.5) in Huimin was higher than that in Saihan, and high concentrations of high airborne particulate matter mainly appeared in winter. From 2014 to 2017, the concentrations of air particulate matters PM_(10) and PM_(2.5) in the two regions showed an overall downward trend.
引文
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[13] 吕喆,魏巍,周颖,等.2015～2016年北京市3次空气重污染红色预警PM2.5成因分析及效果评估[J].环境科学,2019,40(1):1-10.(In English:Lü Z,Wei W,Zhou Y,et al.Cause and effect evaluation of PM2.5 during three red alerts in Beijing from 2015 to 2016[J].Environ Sci,2019,40(1):1-10.)
[14] 杨柳,张军,王莹,等.2016年济南市大气PM2.5对人群超额死亡风险评估[J].环境卫生学杂志,2018,8(3):178-183.(In English:Yang L,Zhang J,Wang Y,et al.Risk assessment on PM2.5 related excess mortality in Jinan,2016[J].J Environ Hyg,2018,8(3):178-183.)
[15] 韩见弘,王佳,燕忠.呼和浩特市空气污染状况分析[J].内蒙古环境科学,2009,21(S1):144-147.
[2] 李丽珍,王浩宇,曹露,王等.浅析中国城市PM2.5的污染现状及控制措施[J].能源与节能,2013(5):69-70.(In English:Li LZ,Wang HY,Cao L,et al.Analysis of PM2.5 pollution situation and control measures in cities of China[J].Energy Energy Conserv,2013(5):69-70.)
[3] 王晨波.PM2.5浓度对能见度影响分析[J].科技信息,2013(15):439-440.(In English:Wang CB.Concentration of PM2.5 on visibility impact analysis[J].Sci Technol Inf,2013(15):439-440.)
[4] 王琼,董小艳,杨璐璐,等.基于北京市大气PM2.5中9种元素的环境健康风险评价[J].环境卫生学杂志,2018,8(3):197-203.(In English:Wang Q,Dong XY,Yang LL,et al.Environment health risk assessment on 9 elements in PM2.5 in Beijing[J].J Environ Hyg,2018,8(3):197-203.)
[5] 吴俊,陈晓东,周连,等.南京市某区大气中PM2.5污染状况及变化趋势分析[J].环境卫生学杂志,2013,3(2):77-79,83.(In English:Wu J,Chen XD,Zhou L,et al.Analysis on the pollution status and change trend of PM2.5 in the atmosphere in Nanjing[J].J Environ Hyg,2013,3(2):77-79,83.)
[6] 段振华,高绪芳,杜慧兰,等.成都市空气PM2.5浓度与呼吸系统疾病门诊人次的时间序列研究[J].现代预防医学,2015,42(4):611-614.(In English:Duan ZH,Gao XF,Du HL,et al.Analysis on the relationship between PM2.5 concentration in ambient air and hospital outpatients with respiratory diseases in Chengdu[J].Mod Prev Med,2015,42(4):611-614.)
[7] 李璇,聂滕,齐珺,等.2013年1月北京市PM2.5区域来源解析[J].环境科学,2015,36(4):1148-1153.(In English:Li X,Nie T,Qi J,et al.Regional source apportionment of PM2.5 in Beijing in January 2013[J].Environ Sci,2015,36(4):1148-1153.)
[8] 中华人民共和国国家质量监督检验检疫总局 .HJ 618—2011环境空气PM10和PM2.5的测定重量法[S].北京:中国标准出版社,2011.
[9] 环境保护部.环境空气质量标准 GB 3095—2012[S].北京:中国环境科学出版社,2012.
[10] US EPA.Air quality criteria for particulate matter[R].Washington DC:WHO,2004.
[11] 蒋润民,敦鑫龙,殷玥,等.PM2.5对循环和呼吸系统的影响、相关机制及研究进展[J].心脏杂志,2018,30(5):609-612.(In English:Jiang RM,Guo XL,Yin Y,et al.Influence of PM2.5 on circulation and respiratory system and its mechanism and research progress[J].Chin Heart J,2018,30(5):609-612.)
[12] 罗达通,张敬巧,刘湛,等.郴州市大气PM2.5碳组分污染特征及其来源分析[J].环境科学研究,2018,31(11):1858-1866.(In English:Luo DT,Zhang JQ,Liu Z,et al.Characteristics and sources of carbonaceous species in atmospheric PM2.5 in Chenzhou City[J].Res Environ Sci,2018,31(11):1858-1866.)
[13] 吕喆,魏巍,周颖,等.2015～2016年北京市3次空气重污染红色预警PM2.5成因分析及效果评估[J].环境科学,2019,40(1):1-10.(In English:Lü Z,Wei W,Zhou Y,et al.Cause and effect evaluation of PM2.5 during three red alerts in Beijing from 2015 to 2016[J].Environ Sci,2019,40(1):1-10.)
[14] 杨柳,张军,王莹,等.2016年济南市大气PM2.5对人群超额死亡风险评估[J].环境卫生学杂志,2018,8(3):178-183.(In English:Yang L,Zhang J,Wang Y,et al.Risk assessment on PM2.5 related excess mortality in Jinan,2016[J].J Environ Hyg,2018,8(3):178-183.)
[15] 韩见弘,王佳,燕忠.呼和浩特市空气污染状况分析[J].内蒙古环境科学,2009,21(S1):144-147.
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