Dust storms in arid and desert areas affect radiation budget,air quality,visibility,enzymatic activities,agricultural products and human health.Due to increased drought and land use changes in recent years,the frequen...Dust storms in arid and desert areas affect radiation budget,air quality,visibility,enzymatic activities,agricultural products and human health.Due to increased drought and land use changes in recent years,the frequency of dust storms occurrence in Iran has been increased.This study aims to identify dust source areas in the Sistan watershed(Iran-Afghanistan borders)-an important regional source for dust storms in southwestern Asia,using remote sensing(RS)and bivariate statistical models.Furthermore,this study determines the relative importance of factors controlling dust emissions using frequency ratio(FR)and weights of evidence(WOE)models and interpretability of predictive models using game theory.For this purpose,we identified 211 dust sources in the study area and generated a dust source distribution map-inventory map-by dust source potential index based on RS data.In addition,spatial maps of topographic factors affecting dust source areas including soil,lithology,slope,Normalized difference vegetation index(NDVI),geomorphology and land use were prepared.The performance of two models(WOE and FR)was evaluated using the area under curve(AUC)of the receiver operating characteristic curve.The results showed that soil,geomorphology and slope exhibited the greatest influence in the dust source areas.The 55.3%(according to FR)and 62.6%(according to WOE)of the total area were classified as high and very high potential dust sources,while both models displayed acceptable accuracy with subsurface levels of 0.704 for FR and 0.751 for WOE,although they predict different fractions of dust potential classes.Based on Shapley additive explanations(SHAP),three factors,i.e.,soil,slope and NDVI have the highest impact on the model's output.Overall,combination of statistic-based predictive models(or data mining models),RS and game theory techniques can provide accurate maps of dust source areas in arid and semi-arid regions,which can be helpful for mitigation of negative effects of dust storms.展开更多
Extensive dustfall collections were carried out from April 2001 to May 2002 in North China. The highest level of dustfall occurred in the Gobi deserts and at the margins of sandy deserts in the region. The iron conten...Extensive dustfall collections were carried out from April 2001 to May 2002 in North China. The highest level of dustfall occurred in the Gobi deserts and at the margins of sandy deserts in the region. The iron content in dustfall in North China varied from 0.6% to 6.0% and there was significant seasonal variation, which indicates the dust sources differed during the year. Although the iron content in dustfall in North China is higher in the Loess Plateau and arable lands and lower in the Gobi and sandy deserts, the total iron deposition was higher in the Gobi desert regions. If the fine particles （PM10） in dustfall in North China are the major contributors of dust transport to eastern China and western parts of the North Pacific, then the annual deposition rates of iron may have been underestimated in previous studies. Our analysis indicates that iron deposition may reach 1.38 × 10^3 to 2.43 × 10^3 kg km^-2 and that most iron deposition occurs in spring and summer. If the more-coarse fractions （PM50） are considered, deposition rates may reach 2.75 × 10^3 and 6.80 × 10^3 kg km^-2, which would represent a large source of iron deposition in eastern China and the western North Pacific.展开更多
This study focused on the contents of the air particulate matter pollution in two districts of Ulaanbaatar and determined the chemical composition of air borne samples and the source of those particles. Samples of fin...This study focused on the contents of the air particulate matter pollution in two districts of Ulaanbaatar and determined the chemical composition of air borne samples and the source of those particles. Samples of fine and coarse fractions of PM were collected using a “Gent” stacked filter unit in two fractions of 0 - 2.2 μm and 2.2 - 10 μm sizes in two semi-residential areas from September 2012 to August 2013. This paper points out that fine and coarse concentration varied seasonally with meteorological changes. In sampling site 3, Zuun Ail (Figure 1) combustion generators generate the majority of pollution around 50.6% of household waste furnace to create high-temperature combustion of 21.6%. However, this net contributes to soil contamination near the lower value (5%) that arises around the vacuum environment in substantial amounts (14%), where is open around the buildings and residential areas, and the soil is considered to be due to the construction. But the data point to the highway in the distance, where is 9% of contamination of all vehicles’ smoke, and exhaust is similar to the data collected in Ulaanbaatar. According to analysis of samples of Nuclear Research Center (NRC) sampling site 2, it shows burning source of Particulate Matter 2.5 pollution in the air is around 25.5% of household waste furnace to create high-temperature product of combustion. But here the very high net contribution to the pollution of soil, is 31.6%. Today’s emerging dust is around 15.2%, showing that motor vehicle pollution causes 19.7%. Since the analysis was done on a sample-by-sample basis, it is possible to estimate the daily contributions of pollution sources and provide useful information based on a limited number of samples in order to address air quality management issues in Ulaanbaatar.展开更多
Even though the biological crusts are critical to dust emissions,no sand and dust forecast model have considered the impacts of the biological crust in dust emission scheme.This situation mainly comes from two scienti...Even though the biological crusts are critical to dust emissions,no sand and dust forecast model have considered the impacts of the biological crust in dust emission scheme.This situation mainly comes from two scientific difficulties:there is no large scale regional biological crust data available that can be used in the forecast model;there is no quantification of how biological crusts impact on sand emission.In this way,we studied the distribution of biological soil crust in sand and dust storm source areas of Central and East Asia using Moderate Resolution Imaging Spectroradiometer satellite surface reflectance data collected in 2000—2019 to determine its potential impact on dust emission according to two empirical schemes.We further evaluated the relationships between soil crust coverage,roughness length,and dust emission to study SDS source areas.We found that biological crust is widely distributed in SDS source areas of Central and East Asia,with coverage rates of 19.8%in Central Asian deserts,23.1%in the Gobi Desert,and 17.3%—32.8%in Chinese deserts(p>0.05).Cyanobacteria and lichen coverage has increased in Chinese deserts,reflecting the recent impacts of the Project of Returning Farmland to Grassland and Farmland to Forests.However,biological soil crust coverage has not increased in Central Asian deserts or the Gobi Desert,and that in Central Asian deserts continues to decrease,demonstrating the complexity of the combined effects of human activities and climate change on its distribution.Biological soil crust increased the roughness length of Central and East Asian SDS source areas by 0.14—0.62 mm.The suppression of dust emission due to biological soil crust did not change among years during the study period.The horizontal and vertical dust flux inhibition coefficient(DFIC)were 2.0—11.0 and 1.7—2.9(p>0.05),respectively,clearly showing a suppressive effect.Improvement of the ecological environment in some deserts can lead to the ability of these crusts to inhibit dust erosion errors that must be considered in the dust emission scheme for areas where crust coverage has improved.展开更多
京津冀地区是我国大气污染严重区域,土壤扬尘颗粒物排放变化研究对于改善京津冀地区空气质量具有重要意义。收集2000—2019年京津冀地区气候、土壤、植被覆盖数据,分析近20年来京津冀地区土壤扬尘颗粒物排放变化,揭示其变化的影响因素...京津冀地区是我国大气污染严重区域,土壤扬尘颗粒物排放变化研究对于改善京津冀地区空气质量具有重要意义。收集2000—2019年京津冀地区气候、土壤、植被覆盖数据,分析近20年来京津冀地区土壤扬尘颗粒物排放变化,揭示其变化的影响因素。结果显示2000—2019年京津冀地区土壤扬尘源总悬浮颗粒物(TSP)排放系数均值为1.79 t km^(-2)a^(-1),其中PM10占8.99%,PM2.5占0.25%。近20年土壤扬尘源TSP排放系数具有下降趋势,PM10和PM2.5排放系数变化过程与TSP一致。上述变化主要受气候因子变化影响,其次受植被覆盖度影响。分析发现近20年来京津冀地区土壤扬尘源TSP排放系数变化主要受年降水量影响。沧州市、天津市和石家庄市土壤扬尘源TSP、PM10和PM2.5排放系数均值较高,张家口市、保定市和沧州市土壤扬尘源TSP排放量占京津冀地区总量的19.18%、12.98%和11.63%。耕地土壤扬尘排放量最大占京津冀地区总量的59.83%,是抑制土壤扬尘源颗粒物排放的重点关注对象,其次为草地占15.66%。2019年邢台市土壤扬尘源PM10排放占观测值比例最高为12.66%,石家庄市和天津市占比也较高分别为11.09%和10.30%,沧州市和邯郸市占比分别为8.63%和8.02%。上述地区环境管理部门均应关注土壤扬尘源颗粒物排放对空气质量的影响。展开更多
基金The study was financially supported by the Fund for Support of Researchers and Technologists of Iran(97022330)Panhellenic Infrastructure for Atmospheric Composition and Climate Change(PANACEA,MIS 5021516)+1 种基金Competitiveness,Entrepreneurship and Innovation(NSRF 2014-2020)co-financed by Greece and the European Union(European Regional Development Fund).
文摘Dust storms in arid and desert areas affect radiation budget,air quality,visibility,enzymatic activities,agricultural products and human health.Due to increased drought and land use changes in recent years,the frequency of dust storms occurrence in Iran has been increased.This study aims to identify dust source areas in the Sistan watershed(Iran-Afghanistan borders)-an important regional source for dust storms in southwestern Asia,using remote sensing(RS)and bivariate statistical models.Furthermore,this study determines the relative importance of factors controlling dust emissions using frequency ratio(FR)and weights of evidence(WOE)models and interpretability of predictive models using game theory.For this purpose,we identified 211 dust sources in the study area and generated a dust source distribution map-inventory map-by dust source potential index based on RS data.In addition,spatial maps of topographic factors affecting dust source areas including soil,lithology,slope,Normalized difference vegetation index(NDVI),geomorphology and land use were prepared.The performance of two models(WOE and FR)was evaluated using the area under curve(AUC)of the receiver operating characteristic curve.The results showed that soil,geomorphology and slope exhibited the greatest influence in the dust source areas.The 55.3%(according to FR)and 62.6%(according to WOE)of the total area were classified as high and very high potential dust sources,while both models displayed acceptable accuracy with subsurface levels of 0.704 for FR and 0.751 for WOE,although they predict different fractions of dust potential classes.Based on Shapley additive explanations(SHAP),three factors,i.e.,soil,slope and NDVI have the highest impact on the model's output.Overall,combination of statistic-based predictive models(or data mining models),RS and game theory techniques can provide accurate maps of dust source areas in arid and semi-arid regions,which can be helpful for mitigation of negative effects of dust storms.
文摘Extensive dustfall collections were carried out from April 2001 to May 2002 in North China. The highest level of dustfall occurred in the Gobi deserts and at the margins of sandy deserts in the region. The iron content in dustfall in North China varied from 0.6% to 6.0% and there was significant seasonal variation, which indicates the dust sources differed during the year. Although the iron content in dustfall in North China is higher in the Loess Plateau and arable lands and lower in the Gobi and sandy deserts, the total iron deposition was higher in the Gobi desert regions. If the fine particles （PM10） in dustfall in North China are the major contributors of dust transport to eastern China and western parts of the North Pacific, then the annual deposition rates of iron may have been underestimated in previous studies. Our analysis indicates that iron deposition may reach 1.38 × 10^3 to 2.43 × 10^3 kg km^-2 and that most iron deposition occurs in spring and summer. If the more-coarse fractions （PM50） are considered, deposition rates may reach 2.75 × 10^3 and 6.80 × 10^3 kg km^-2, which would represent a large source of iron deposition in eastern China and the western North Pacific.
文摘This study focused on the contents of the air particulate matter pollution in two districts of Ulaanbaatar and determined the chemical composition of air borne samples and the source of those particles. Samples of fine and coarse fractions of PM were collected using a “Gent” stacked filter unit in two fractions of 0 - 2.2 μm and 2.2 - 10 μm sizes in two semi-residential areas from September 2012 to August 2013. This paper points out that fine and coarse concentration varied seasonally with meteorological changes. In sampling site 3, Zuun Ail (Figure 1) combustion generators generate the majority of pollution around 50.6% of household waste furnace to create high-temperature combustion of 21.6%. However, this net contributes to soil contamination near the lower value (5%) that arises around the vacuum environment in substantial amounts (14%), where is open around the buildings and residential areas, and the soil is considered to be due to the construction. But the data point to the highway in the distance, where is 9% of contamination of all vehicles’ smoke, and exhaust is similar to the data collected in Ulaanbaatar. According to analysis of samples of Nuclear Research Center (NRC) sampling site 2, it shows burning source of Particulate Matter 2.5 pollution in the air is around 25.5% of household waste furnace to create high-temperature product of combustion. But here the very high net contribution to the pollution of soil, is 31.6%. Today’s emerging dust is around 15.2%, showing that motor vehicle pollution causes 19.7%. Since the analysis was done on a sample-by-sample basis, it is possible to estimate the daily contributions of pollution sources and provide useful information based on a limited number of samples in order to address air quality management issues in Ulaanbaatar.
基金supported by the National Key Project of the Ministry of Science and Technology of China(2019YFC0214601)Foundation for Development of Science and Technology of Chinese Academy of Meteorological Sciences(2018KJ048,2017Z01).
文摘Even though the biological crusts are critical to dust emissions,no sand and dust forecast model have considered the impacts of the biological crust in dust emission scheme.This situation mainly comes from two scientific difficulties:there is no large scale regional biological crust data available that can be used in the forecast model;there is no quantification of how biological crusts impact on sand emission.In this way,we studied the distribution of biological soil crust in sand and dust storm source areas of Central and East Asia using Moderate Resolution Imaging Spectroradiometer satellite surface reflectance data collected in 2000—2019 to determine its potential impact on dust emission according to two empirical schemes.We further evaluated the relationships between soil crust coverage,roughness length,and dust emission to study SDS source areas.We found that biological crust is widely distributed in SDS source areas of Central and East Asia,with coverage rates of 19.8%in Central Asian deserts,23.1%in the Gobi Desert,and 17.3%—32.8%in Chinese deserts(p>0.05).Cyanobacteria and lichen coverage has increased in Chinese deserts,reflecting the recent impacts of the Project of Returning Farmland to Grassland and Farmland to Forests.However,biological soil crust coverage has not increased in Central Asian deserts or the Gobi Desert,and that in Central Asian deserts continues to decrease,demonstrating the complexity of the combined effects of human activities and climate change on its distribution.Biological soil crust increased the roughness length of Central and East Asian SDS source areas by 0.14—0.62 mm.The suppression of dust emission due to biological soil crust did not change among years during the study period.The horizontal and vertical dust flux inhibition coefficient(DFIC)were 2.0—11.0 and 1.7—2.9(p>0.05),respectively,clearly showing a suppressive effect.Improvement of the ecological environment in some deserts can lead to the ability of these crusts to inhibit dust erosion errors that must be considered in the dust emission scheme for areas where crust coverage has improved.
文摘京津冀地区是我国大气污染严重区域,土壤扬尘颗粒物排放变化研究对于改善京津冀地区空气质量具有重要意义。收集2000—2019年京津冀地区气候、土壤、植被覆盖数据,分析近20年来京津冀地区土壤扬尘颗粒物排放变化,揭示其变化的影响因素。结果显示2000—2019年京津冀地区土壤扬尘源总悬浮颗粒物(TSP)排放系数均值为1.79 t km^(-2)a^(-1),其中PM10占8.99%,PM2.5占0.25%。近20年土壤扬尘源TSP排放系数具有下降趋势,PM10和PM2.5排放系数变化过程与TSP一致。上述变化主要受气候因子变化影响,其次受植被覆盖度影响。分析发现近20年来京津冀地区土壤扬尘源TSP排放系数变化主要受年降水量影响。沧州市、天津市和石家庄市土壤扬尘源TSP、PM10和PM2.5排放系数均值较高,张家口市、保定市和沧州市土壤扬尘源TSP排放量占京津冀地区总量的19.18%、12.98%和11.63%。耕地土壤扬尘排放量最大占京津冀地区总量的59.83%,是抑制土壤扬尘源颗粒物排放的重点关注对象,其次为草地占15.66%。2019年邢台市土壤扬尘源PM10排放占观测值比例最高为12.66%,石家庄市和天津市占比也较高分别为11.09%和10.30%,沧州市和邯郸市占比分别为8.63%和8.02%。上述地区环境管理部门均应关注土壤扬尘源颗粒物排放对空气质量的影响。
