渭河流域PM2.5时空演化及人口暴露风险

doi:10.16258/j.cnki.1674-5906.2023.06.009

生态环境学报 ›› 2023, Vol. 32 ›› Issue (6): 1078-1088.DOI: 10.16258/j.cnki.1674-5906.2023.06.009

渭河流域PM_2.5时空演化及人口暴露风险

董洁芳¹(), 邓椿²^,^*(), 张仲伍³

1.运城学院黄河文化生态研究院/运城学院文化旅游系，山西运城 044000
2.西北大学城市与环境学院，陕西西安 710127
3.山西师范大学地理科学学院，山西太原 030000

收稿日期:2022-09-30 出版日期:2023-06-18 发布日期:2023-09-01
通讯作者: *邓椿（1981年生），男，博士研究生，主要研究方向为水文生态。E-mail: 202010225@stumail.nwu.edu.cn
作者简介:董洁芳（1984年生），女，副教授，博士，硕士研究生导师，主要研究方向为区域经济与生态旅游开发。E-mail: dongjiefang-2005@163.com
基金资助:
山西省黄河文化生态研究院项目(HH202101);运城学院旅游管理重点学科项目(XK-2021031)

Spatio-temporal Evolution and Population Exposure Risk to PM_2.5 in the Weihe River Basin

DONG Jiefang¹(), DENG Chun²^,^*(), ZHANG Zhongwu³

1. Yellow River Cultural and Ecological Research Institute, Yuncheng University/Department of Cultural and Tourism, Yuncheng University,Yuncheng 044000, P. R. China
2. College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, P. R. China
3. School of Geography Sciences, Shanxi Normal University, Taiyuan 030000, P. R. China

Received:2022-09-30 Online:2023-06-18 Published:2023-09-01

摘要/Abstract

摘要：

研究PM_2.5时空演化及人口暴露风险，对于环境风险评价及人居环境改善、政府环保部门制定针对性的空气污染防控政策具有重要意义。渭河流域是国家重要工业基地，也是国家级城市群和关中—天水国家级经济区的核心区域，降低该区域PM_2.5人口暴露风险是实现高质量发展的必然途径。基于渭河流域2000—2020年PM_2.5遥感反演数据和人口格网分布数据，测算人口暴露风险指数。采用Theil-Sen Median与Mann Kendall检验法，分别识别PM_2.5质量浓度值和人口暴露风险指数时间演化特征，并通过GIS空间探索工具，分析其空间变化特征。结果表明，（1）2000—2020年渭河流域PM_2.5年均质量浓度为47.2 μg·m^-3，最高值为2013年的57.6 μg·m^-3，最低值为2020年的31.8 μg·m^-3，呈现先上升后下降的变化趋势。趋势显著性检验发现，渭河流域PM_2.5污染情况呈现好转趋势。（2）PM_2.5年均质量浓度空间分布呈东高西低特征，高值主要集中在流域下游地区，如西安市、咸阳市和渭南市等。低值主要分布在流域中上游地区，如天水市、定西市、平凉市等。（3）2000—2020年渭河流域PM_2.5人口暴露风险等级总体呈下降趋势，但历年暴露于35 μg·m^-3以上人数占比均值高达96.2%。2000—2003年，2005—2014年间，渭河流域100%人口暴露于浓度值35 μg·m^-3以上。但高风险区域面积从2000年的20.6%下降为2020年的17.1%。（4）2000—2020年渭河流域历年PM_2.5人口暴露风险等级均呈现东高西低的空间格局，且空间差异较大，“热点”型主要集中渭城区、秦都区、未央区等城市建成区单元，“冷点”型主要分布在定西市、天水市、平凉市等流域西部海拔较高区域。研究结论可为渭河流域制定联防联控的PM_2.5污染治理政策提供科学依据。

关键词: PM_2.5, 人口暴露风险, 遥感数据, 时空特征, 渭河流域

Abstract:

The investigation of the spatio-temporal evolution of PM_2.5 and its associated risk of population exposure holds paramount significance for environmental risk assessment, habitat enhancement, and the development of precise air pollution prevention and control policies by governmental environmental protection agencies. The Weihe River Basin serves as a crucial industrial base of the country and represents an important part of a national urban agglomeration, as well as the Guanzhong-Tianshui national economic zone. Mitigating the risk of population exposure to PM_2.5in this region is an inevitable step towards high-quality development. Based on remote sensing inversion data of PM_2.5 and population grid distribution data in the Weihe River Basin from 2000 to 2020, the population exposure risk index was calculated. The temporal evolution characteristics of PM_2.5 quality concentration values and population exposure risk indices were identified using the Theil-Sen Median and Mann Kendall tests, respectively. Additionally, the spatial variation characteristics of these variables were analyzed through the utilization of GIS spatial exploration tools. The results showed that (1) the annual average quality concentration of PM_2.5 in the Weihe River Basin was 47.2 μg·m^-3 from 2000 to 2020. The highest value of 57.6 μg·m^-3 was recorded in 2013, while the lowest value of 31.8 μg·m^-3 was observed in 2020. This indicated a trend of initial increase followed by a decrease. Significance testing revealed a decreasing trend in PM_2.5 pollution levels in the Weihe River Basin. (2) The spatial distribution of annual average PM_2.5 quality concentrations exhibited distinct characteristics, with higher values in the eastern region and lower values in the western region. High values were mainly distributed in the lower reaches of the river basin, such as Xi’an, Xianyang and Weinan. Low values were mainly distributed in the middle and upper reaches of the river basin, such as Tianshui City, Dingxi City, Pingliang City, etc. (3) Overall, the risk level of PM_2.5 population exposure in the Weihe River Basin showed a downward trend from 2000 to 2020. However, the average proportion of the population exposed to more than 35 μg·m^-3 over the years was as high as 96.2%. Between 2000 and 2003, as well as from 2005 to 2014, the entire population in the Weihe River Basin experienced exposure to concentrations of 35 μg·m^-3 or higher. Nevertheless, the extent of high-risk areas decreased from 20.6% in 2000 to 17.1% in 2020. (4) The risk level of PM_2.5 population exposure demonstrated a spatial pattern with higher levels in the east and lower levels in the west, displaying significant spatial variation in the Weihe River Basin from 2000 to 2020. Urban built-up areas such as Weicheng, Qindu, and Weiyang predominantly exhibited a “hot spot” type with higher concentrations. Conversely, the “cold spot” type was mainly concentrated in higher elevation areas in the western part of the basin, including Dingxi, Tianshui, and Pingliang. The findings of this study provide a scientific foundation for formulating a collaborative prevention and control policy in managing PM_2.5 pollution within the Weihe River Basin.

Key words: PM_2.5, population exposure risk, remote sensing data, spatiotemporal characteristics, Weihe River Basin

中图分类号:

董洁芳, 邓椿, 张仲伍. 渭河流域PM_2.5时空演化及人口暴露风险[J]. 生态环境学报, 2023, 32(6): 1078-1088.

DONG Jiefang, DENG Chun, ZHANG Zhongwu. Spatio-temporal Evolution and Population Exposure Risk to PM_2.5 in the Weihe River Basin[J]. Ecology and Environment, 2023, 32(6): 1078-1088.

图/表 10

表1 Mann-Kendall检验趋势显著性类别

Table 1 Mann-Kendall test trend categories

β值	Z值	趋势特征
β>0	2.58<Z	极显著增加
	1.96<Z≤2.58	显著增加
	1.65<Z≤1.96	微显著增加
	Z≤1.65	不显著增加
β=0	Z	无变化
β<0	Z≤1.65	不显著减少
	1.65<Z≤1.96	微显著减少
	1.96<Z≤2.58	显著减少
	2.58<Z	极显著减少

图1 2000—2020年渭河流域PM2.5质量浓度分区间面积占比

Figure 1 Area proportion of PM2.5 concentration in Weihe River Basin from 2000 to 2020

图2 2000—2020年渭河流域PM2.5质量浓度变化趋势

Figure 2 Trends of PM2.5 concentration by types in the Weihe River basin from 2000 to 2020

图3 2000—2020年渭河流域PM2.5质量浓度空间分布

Figure 3 Spatial distribution of PM2.5 annual concentration in the Weihe River basin from 2000 to 2020

图4 2000—2020年渭河流域区PM2.5质量浓度空间集聚图

Figure 4 Spatial clusters of PM2.5 annual concentration in the Weihe River basin from 2000 to 2020

图5 2000—2020年渭河流域PM2.5质量浓度分区间人口暴露数量占比

Figure 5 The proportion of the population exposure risk to PM2.5 concentrations in the Weihe River basin from 2000 to 2020

图6 2000—2020年渭河流域人口暴露高等级风险区面积占比趋势

Figure 6 Trend of area proportion of population exposure high-level risk to PM2.5 in the Weihe River basin from 2000 to 2020

图7 2000—2020年渭河流域PM2.5人口风险暴露指数线性变化趋势

Figure 7 Trend of population exposure risk to PM2.5 by type in the Weihe River basin from2000 to 2020

图8 2000—2020年渭河流域PM2.5人口暴露风险等级空间格局

Figure 8 Spatial pattern of population exposure risk to PM2.5 in the Weihe River basin from 2000 to 2020

图9 2000—2020年渭河流域PM2.5人口暴露风险局部空间集聚类型

Figure 9 Spatial clusters of population exposure risk to PM2.5 n the Weihe River basin from 2000 to 2020

参考文献 42

[1]	DONKELAAR A, MARTIN R V, LEVY R C, et al., 2011. Satellite-based estimates of ground-level fine particulate matter during extreme events: A case study of the Moscow fires in 2010[J]. Atmospheric Environment, 45(34): 6225-6232. DOI URL
[2]	BECKERMAN B S, JERRETT M, SERRE M, et al., 2013. A hybrid approach to estimating national scale spatiotemporal variability of PM_2.5 in the contiguous United States[J]. Environmental Science & Technology, 47(13): 7233-7241. DOI URL
[3]	YANG Y, CHRISTAKOS G, 2015. Spatiotemporal characterization of ambient PM_2.5 concentrations in Shandong province (China)[J]. Environmental Science & Technology, 49(22): 13431-13438. DOI URL
[4]	GAO M L, CAO J J, SETO E, 2015. A distributed network of low-cost continuous reading sensors to measure spatiotemporal variations of PM_2.5 in Xi’an, China[J]. Environmental Pollution, 199: 56-65. DOI URL
[5]	HAN L J, ZHOU W Q, LI W F, 2015. City as a major source area of fine particulate (PM_2.5) in China[J]. Environmental Pollution, 206: 183-187. DOI PMID
[6]	LIU X J, XIA S Y, YANG Y, et al., 2020. Spatiotemporal dynamics and impacts of socioeconomic and natural conditions on PM_2.5 in the Yangtze River economic belt[J]. Environmental Pollution, 263(Part A): 114569. DOI URL
[7]	WEI J, HUANG W, LI Z Q, et al., 2019. Estimating 1-km-resolution PM_2.5 concentrations across China using the space-time random forest approach[J]. Remote Sensing of Environment, 231: 111221. DOI URL
[8]	XU W J, ZENG Z T, XU Z Y, et al., 2020. Public health benefits of optimizing urban industrial land layout-The case of Changsha, China[J]. Environmental Pollution, 263(Part B): 114388. DOI URL
[9]	YAN R H, PENG X, LIN W, et al., 2022. Trends and challenges regarding the source-specific health risk of PM_2.5-bound metals in a Chinese megacity from 2014 to 2020[J]. Environment Science Technology, 56(11): 6996-7005. DOI URL
[10]	ZHANG L C, AN J, LIU M Y, et al., 2020. Spatiotemporal variations and influencing factors of PM_2.5 concentrations in Beijing, China[J]. Environmental Pollution, 262: 114276. DOI URL
[11]	陈登帅, 李晶, 杨晓楠, 等, 2018. 渭河流域生态系统服务权衡优化研究[J]. 生态学报, 38(9): 3260-3271.
	CHEN D S, LI J, YANG X N, et al., 2018. Trade-offs and optimization among ecosystem services in the Weihe River Basin[J]. Acta Ecologica Sinica, 38(9): 3260-3271.
[12]	邓芙蓉, 王欣, 苏会娟, 等, 2009. 北京市某城区儿童大气PM_2.5个体暴露水平及影响因素研究[J]. 环境与健康杂志, 26(9): 762-765.
	DENG F R, WANG X, SU H J, et al., 2009. Personal exposure to PM_2.5 of children living near traffic road and the influencing factors in Beijing[J]. Journal of Environment and Health, 26(9): 762-765.
[13]	董海燕, 潘耀忠, 朱秀芳, 等, 2022. 多因子贡献率权重的城市精细人口空间化方法: 以北京市为例[J]. 北京师范大学学报(自然科学版), 58(1): 135-142.
	DONG H Y, PAN Y Z, ZHU X F, et al., 2022. Spatial method of urban fine population with multifactor contribution rate weight: Case of Beijing[J]. Journal of Beijing Normal University (Natural Science), 58(1): 135-142.
[14]	郝永佩, 宋晓伟, 赵文珺, 等, 2022. 汾渭平原大气污染时空分布及相关因子分析[J]. 生态环境学报, 31(3): 512-523. DOI
	HAO Y P, SONG X W, ZHAO W J, et al., 2022. Spatiotemporal distribution of air pollution and correlation factors in Fenwei Plain[J]. Ecology and Environmental Sciences, 31(3): 512-523.
[15]	郭雯雯, 陈永金, 刘阁, 等, 2020. 2016-2019年长江中游城市群空气质量时空变化特征及影响因素分析[J]. 生态环境学报, 29(10): 2034-2044. DOI
	GUO W W, CHEN Y J, LIU G, et al., 2020. Analysis on the characteristics and influencing factors of air quality of urban agglomeration in the middle reaches of the Yangtze River in 2016 to 2019[J]. Ecology and Environmental Sciences, 29(10): 2034-2044.
[16]	黄小刚, 赵景波, 曹军骥, 等, 2020. 长江经济带PM_2.5分布格局演变及其影响因素[J]. 环境科学, 41(3): 1013-1024.
	HUANG X G, ZHAO J B, CAO J J, et al., 2020. Evolution of the distribution of PM_2.5concentration in the Yangtze River economic belt and its influencing factors[J]. Environmental Science, 41(3): 1013-1024.
[17]	黄晓军, 祁明月, 李艳雨, 等, 2020. 关中地区PM_2.5时空演化及人口暴露风险[J]. 环境科学, 41(12): 5245-5255.
	HUANG X J, QI M Y, LI Y Y, et al., 2020. Spatio-temporal evolution and population exposure risk to PM_2.5in the Guanzhong area[J]. Environmental Science, 41(12): 5245-5255. DOI URL
[18]	柯钊跃, 王佳, 郑君瑜, 等, 2011. 广州市学龄儿童在校期间PM_2.5暴露水平评价[J]. 中国环境科学, 31(10): 1618-1624.
	KE Z Y, WANG J, ZHENG J Y, et al., 2011. PM_2.5exposure assessment of school children at a primary school in Guangzhou, China[J]. China Environmental Science, 31(10): 1618-1624.
[19]	李佳雯, 2020. 基于多源卫星遥感的长三角PM_2.5污染人口暴露度时空分布研究[D]. 南京: 南京信息工程大学:12-26.
	LI J W, 2020. Spatial-temporal distribution characteristics of PM_2.5pollution exposure based on multi-source satellite Remote Sensing of population in the Yangtze River delta[D]. Nanjing: Nanjing University of Information Science and Technology:12-26.
[20]	廖林渲, 陈静, 2022. 河南省PM_2.5暴露人口时空特征及影响因素研究[J]. 福建师范大学学报 (自然科学版), 38(1): 59-68.
	LIAO L X, CHEN J, 2022. Study on the spatiotemporal characteristics and influencing factors of population exposure to particulate matter (PM_2.5) in Henan province[J]. Journal of Fujian Normal University (Natural Science Edition), 38(1): 59-68.
[21]	林丹淳, 谭敏, 刘凯, 等, 2020. 代表性人口空间分布数据集的精度评价——以2010年广东省为例[J]. 热带地理, 40(2): 346-356. DOI
	LIN D C, TAN M, LIU K, et al., 2020. Accuracy comparison of four gridded population datasets in Guangdong province, China[J]. Tropical Geography, 40(2): 346-356. DOI
[22]	林金煌, 陈文惠, 张岸, 2020. 2019年北京市PM_2.5人群暴露剂量特征分析[J]. 地球信息科学学报, 22(12): 2348-2357. DOI
	LIN J H, CHEN W H, ZHANG A, 2020. Analysis of PM_2.5 population exposure doses characteristics in Beijing in 2019[J]. Journal of Geo-information Science, 22(12): 2348-2357.
[23]	刘修岩, 李松林, 秦蒙, 2016. 开发时滞、市场不确定性与城市蔓延[J]. 经济研究, 51(8): 159-171, 186.
	LIU X Y, LI S L, QIN M, 2016. Development lag, market uncertainty and urban sprawl[J]. Economic Research Journa, l51(8): 159-171, 186.
[24]	宓科娜, 庄汝龙, 梁龙武, 等, 2018. 长三角PM_2.5时空格局演变与特征——基于2013-2016年实时监测数据[J]. 地理研究, 37(8): 1641-1654. DOI
	MI K N, ZHUANG R L, LIANG L W, et al., 2018. Spatio-temporal evolution and characteristics of PM_2.5 in the Yangtze River Delta based on real-time monitoring data during 2013-2016[J]. Geographical Research, 37(8): 1641-1654.
[25]	慕航, 何超, 阮秋明, 等, 2021. “一带一路”沿线国家PM_2.5污染与人口暴露风险的时空分布特征[J]. 环境科学学报, 41(6): 2229-2240.
	MU H, HE C, RUAN Q M, et al., 2021. Spatiotemporal distribution characteristics and population exposure risks to PM_2.5in countries along the Belt and Road[J]. Acta Scientiae Circumstantiae, 41(6): 2229-2240.
[26]	王睿哲, 2021. 关中平原城市群大气污染物浓度模拟及人口暴露研究[D]. 西安: 西安科技大学:65-68.
	WANG R Z, 2021. Research on air pollutant concentration simulation and population exposure in Guanzhong Plain urban agglomeration[D]. Xi’an: Xi’an University of Science and Technology:65-68.
[27]	王钊, 韩斌, 倪天茹, 等, 2013. 天津市某社区老年人PM_2.5暴露痕量元素健康风险评估[J]. 环境科学研究, 26(8): 913-918.
	WANG Z, HAN B, NI T R, et al., 2013. Health risk assessment of trace elements of PM_2.5exposure for the elderly subpopulation in Tianjin, China[J]. Research of Environmental Sciences, 26(8): 913-918.
[28]	肖嘉玉, 何超, 慕航, 等, 2021. 中国城市空气污染时空分布格局和人口暴露风险[J]. 地理科学进展, 40(10): 1650-1663. DOI
	XIAO J Y, HE C, MU H, et al., 2021. Spatiotemporal pattern and population exposure risks of air pollution in Chinese urban areas[J]. Progress in Geography, 40(10): 1650-1663. DOI
[29]	谢杨, 戴瀚程, 花岡達也, 等, 2016. PM_2.5污染对京津冀地区人群健康影响和经济影响[J]. 中国人口·资源与环境, 26(11): 19-27.
	XIE Y, DAI H C, HANAOKA T, et al., 2016. Health and economic impacts of PM_2.5 pollution in Beijing-Tianjin-Hebei area[J]. China Population, Resources and Environment, 26(11): 19-27.
[30]	徐建华, 2017. 现代地理学中的数学方法[M]. 第3版. 北京: 高等教育出版社: 225-228.
	XU J H, 2017. Mathematical methods in contemporary geography[M]. Third Edition. Beijing: Higher Education Press: 225-228.
[31]	徐宗学, 刘麟菲, 2021. 渭河流域水生态系统健康评价[J]. 人民黄河, 43(10): 40-43, 50.
	XU Z X, LIU L F, 2021. Assessment on the aquatic ecosystem health of the Weihe River Basin based on periphyton[J]. Yellow River, 43(10): 40-43, 50.
[32]	杨晴青, 刘倩, 尹莎, 等, 2019. 秦巴山区乡村交通环境脆弱性及影响因素——以陕西省洛南县为例[J]. 地理学报, 74(6): 1236-1251. DOI
	YANG Q Q, LIU Q, YIN S, et al., 2019. Vulnerability and influencing factors of rural transportation environment in Qinling-Daba mountainous areas:A case study of Luonan county in Shaanxi province[J]. Acta Geographica Sinica, 74(6): 1236-1251.
[33]	么相姝, 赵文吉, 杨振宇, 等, 2021. 基于Ward系统聚类的京津冀城市群空气质量时空变化特征与成因分析[J]. 生态环境学报, 30(2): 340-350. DOI
	YAO X S, ZHAO W J, YANG Z Y, et al., 2021. Spatial-temporal variation characteristics of air quality and its influencing factors of the Beijing-Tianjin-Hebei urban agglomeration based on ward hierarchical clustering[J]. Ecology and Environmental Sciences, 30(2): 340-350.
[34]	张亮林, 潘竟虎, 2021. 全球PM_2.5人口暴露风险时空格局[J]. 中国环境科学, 41(11): 5391-5404.
	ZHANG L L, PAN J H, 2021. Spatial-temporal pattern of population exposure risk to PM_2.5in global[J]. China Environmental Science, 41(11): 5391-5404.
[35]	张亮林, 潘竟虎, 2020. 中国PM_2.5人口暴露风险时空格局[J]. 中国环境科学, 40(1): 1-12.
	ZHANG L L, PAN J H, 2020. Spatial-temporal pattern of population exposure risk to PM_2.5 in China[J]. China Environmental Science, 40(1): 1-12.
[36]	张西雅, 扈海波, 2018. 基于多源数据的北京地区PM_2.5暴露风险评估[J]. 北京大学学报 (自然科学版), 54(5): 1103-1113.
	ZHANG X Y, HU H B, 2018. Risk assessment of exposure to PM_2.5in Beijing using multi-source data[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 54(5): 1103-1113.
[37]	张音, 古丽贤·吐尔逊拜, 苏里坦, 等, 2019. 近60 a来新疆不同海拔气候变化的时空特征分析[J]. 干旱区地理, 42(4): 822-829.
	ZHANG Y, TUERXUNBAI G LX, SU L T, et al., 2019. Spatial and temporal characteristics of climate change at different altitudes in Xinjiang in the past 60 years[J]. Arid Land Geography, 42(4): 822-829.
[38]	赵辉, 郑有飞, 张誉馨, 等, 2020. 京津冀大气污染的时空分布与人口暴露[J]. 环境科学学报, 40(1): 1-12.
	ZHAO H, ZHENG Y F, ZHANG Y X, et al., 2020. Spatiotemporal distribution and population exposure of air pollution in Beijing-Tianjin-Hebei region[J]. Acta Scientiae Circumstantiae, 40(1): 1-12.
[39]	中华人民共和国环境保护部, 2012. 环境空气质量指数 (AQI) 技术规定(试行): HJ 633—2012[S]. 北京: 环境保护部: 1-6.
	Ministry of Ecology and Environment of the People’s Republic of China, 2012. Technical Regulation on Ambient Air Quality Index (on trial): HJ 633—2012[S]. Beijing: Ministry of Ecology and Environment: 1-6.
[40]	周亮, 周成虎, 杨帆, 等, 2017. 2000-2011年中国PM_2.5时空演化特征及驱动因素解析[J]. 地理学报, 72(11): 2079-2092. DOI
	ZHOU L, ZHOU C H, YANG F, et al., 2017. Spatio-temporal evolution and the influencing factors of PM_2.5 in China between 2000 and 2011[J]. Acta Geographica Sinica, 72(11): 2079-2092.
[41]	周旗, 张海宁, 任源鑫, 2020. 1961-2016年渭河流域极端降水事件研究[J]. 地理科学, 40(5): 833-841. DOI
	ZHOU Q, ZHANG H N, REN Y X, 2020. Extreme precipitation events in the Weihe River Basin from 1961 to 2016[J]. Scientia Geographica Sinica, 40(5): 833-841. DOI
[42]	邹滨, 彭芬, 焦利民, 等, 2013. 高分辨率人口空气污染暴露GIS空间区划研究[J]. 武汉大学学报 (信息科学版), 38(3): 334-338.
	ZOU B, PENG F, JIAO L M, et al., 2013. GIS aided spatial zoning of high-resolution population exposure to air pollution[J]. Geomatics and Information Science of Wuhan University, 38(3): 334-338.

渭河流域PM_2.5时空演化及人口暴露风险

Spatio-temporal Evolution and Population Exposure Risk to PM_2.5 in the Weihe River Basin

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 10

参考文献 42

相关文章 13

编辑推荐

Metrics

本文评价

[1]	巫晨煜, 许帆帆, 魏士博, 樊晶晶, 刘观鹏, 王坤. 渭河流域地表植被覆盖对气候变化的响应研究[J]. 生态环境学报, 2023, 32(5): 835-844.
[2]	李建辉, 党争, 陈琳. 黄河几字弯都市圈PM_2.5时空特征及影响因素分析[J]. 生态环境学报, 2023, 32(4): 697-705.
[3]	张莉, 李铖, 谭皓泽, 韦家怡, 程炯, 彭桂香. 广州典型城市林地对大气颗粒物的削减效应及影响因素[J]. 生态环境学报, 2023, 32(2): 341-350.
[4]	江明, 张子洋, 李婷婷, 林勃机, 张正恩, 廖彤, 袁鸾, 潘苏红, 李军, 张干. 基于氮同位素的珠三角典型地区大气PM_2.5中NH₄⁺来源解析[J]. 生态环境学报, 2022, 31(9): 1840-1848.
[5]	苏泳松, 宋松, 陈叶, 叶子强, 钟润菲, 王昭尧. 珠江三角洲人类活动净氮输入时空特征及其影响因素[J]. 生态环境学报, 2022, 31(8): 1599-1609.
[6]	魏小锋, 韩红, 闫学军, 王在峰, 李圣增, 田勇, 梁第, 马明亮, 张桂芹. 基于卫星遥感与CMB模型的济南市冬季重污染过程PM_2.5溯源分析[J]. 生态环境学报, 2022, 31(6): 1175-1183.
[7]	王薇, 程歆玥. 合肥市不同功能街道峡谷PM_2.5和PM₁₀时空分布特征及影响因素分析[J]. 生态环境学报, 2022, 31(3): 524-534.
[8]	赵锐, 詹梨苹, 周亮, 张军科. 地理探测联合地理加权岭回归的PM_2.5驱动因素分析[J]. 生态环境学报, 2022, 31(2): 307-317.
[9]	蒋斌, 陈多宏, 张涛, 袁鸾, 周炎, 沈劲, 张春林, 王伯光. 华南水稻秸秆焚烧期碳质气溶胶组分特征及源贡献评估[J]. 生态环境学报, 2022, 31(12): 2358-2366.
[10]	邢冉, 沈国锋, 程和发, 陶澍. 东北地区农村生活能源结构变迁及其对区域污染物排放的影响[J]. 生态环境学报, 2022, 31(12): 2367-2373.
[11]	曹云, 孙应龙, 姜月清, 万君. 黄河流域净生态系统生产力的时空分异特征及其驱动因子分析[J]. 生态环境学报, 2022, 31(11): 2101-2110.
[12]	李圣增, 郝赛梅, 谭路遥, 张怀成, 徐标, 谷树茂, 潘光, 王淑妍, 闫怀忠, 张桂芹. 济南市PM_2.5中二次组分的时空变化特征及其影响因素[J]. 生态环境学报, 2022, 31(1): 100-109.
[13]	王薇, 程歆玥, 胡春, 夏斯涵, 王甜. 城市街道峡谷PM_2.5时空分布特征与空气质量评价——以合肥市长淮街道为例[J]. 生态环境学报, 2021, 30(11): 2157-2164.

渭河流域PM2.5时空演化及人口暴露风险

Spatio-temporal Evolution and Population Exposure Risk to PM2.5 in the Weihe River Basin

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 10

参考文献 42

相关文章 13

编辑推荐

Metrics

本文评价

渭河流域PM_2.5时空演化及人口暴露风险

Spatio-temporal Evolution and Population Exposure Risk to PM_2.5 in the Weihe River Basin