The purpose of this study was to establish a method able to accurately estimate the long-term exposure levels of individuals to fine particulate matter (PM2.5) in Jiujiang City (China) by constructing land use regress...The purpose of this study was to establish a method able to accurately estimate the long-term exposure levels of individuals to fine particulate matter (PM2.5) in Jiujiang City (China) by constructing land use regression (LUR) models. Subsequently, the accuracy of models was further verified. PM2.5 concentrations were continuously collected daily from seven monitoring stations for the construction of daily LUR models from September 1 to 14, 2023. The constructed models used PM2.5 concentrations as the dependent variable, while land use, elevation, population density and road length were used as the predictive variables. Subsequently, twenty volunteers were invited to participate, with their daily PM2.5 exposure estimated based on their work address and home address, allowing their average exposure levels to be calculated. Furthermore, volunteers wore portable PM2.5 detectors continuously for a 14-day period and the average measured PM2.5 level was used as a comparative standard. Results showed that the adjusted R2 values for the 14 daily models ranged from 0.85 to 0.94, with the R2 values generated from leave-one-out-cross-validation tests all greater than 0.61, indicating good prediction accuracy. No significant differences were observed in the measurement accuracy of the LUR modeling method and measurements using a portable PM2.5 detector (p > 0.05). This study aimed to develop a novel method for the accurate and convenient measurement of individual long-term PM2.5 exposure levels for epidemiological studies in urban environments comparable to that of Jiujiang city.展开更多
Land use regression （LUR） model was employed to predict the spatial concentration distribution of NO2 and PM10 in the Tianjin region based on the environmental air quality monitoring data. Four multiple linear regre...Land use regression （LUR） model was employed to predict the spatial concentration distribution of NO2 and PM10 in the Tianjin region based on the environmental air quality monitoring data. Four multiple linear regression （MLR） equations were established based on the most significant variables for NO2 in heating season （R2 = 0.74）, and non-heating season （R2 = 0.61） in the whole study area; and PM10 in heating season （R2 = 0.72）, and non-heating season （R2 = 0.49）. Maps of spatial concentration distribution for NO2 and PM10 were obtained based on the MLR equations （resolution is 10 krn）. Intercepts of MLR equations were 0.050 （NOz, heating season）, 0.035 （NO2, non-heating season）, 0.068 （PM10, heating season）, and 0.092 （PM10, non-beating season） in the whole study area. In the central area of Tianjin region, the intercepts were 0.042 （NO2, heating season）, 0.043 （NO2, non-heating season）, 0.087 （PM10, heating season）, and 0.096 （PMl0, non-heating season）. These intercept values might imply an area＇s background concentrations. Predicted result derived from LUR model in the central area was better than that in the whole study area. Rz values increased 0.09 （heating season） and 0.18 （non-heating season） for NO2, and 0.08 （heating season） and 0.04 （non-heating season） for PMl0. In terms of R2, LUR model performed more effectively in heating season than non-heating season in the study area and gave a better result for NOz compared with PM10.展开更多
Advancing the understanding of the spatial aspects of air pollution in the city regional environment is an area where improved methods can be of great benefit to exposure assessment and policy support. We created land...Advancing the understanding of the spatial aspects of air pollution in the city regional environment is an area where improved methods can be of great benefit to exposure assessment and policy support. We created land use regression （LUR） models for SO2, NO2 and PMI0 for Tianjin, China. Traffic volumes, road networks, land use data, population density, meteorological conditions, physical conditions and satellite-derived greenness, brightness and wetness were used for predicting SOa, NO2 and PMt0 concentrations. We incorporated data on industrial point sources to improve LUR model performance. In order to consider the impact of different sources, we calculated the PSIndex, LSIndex and area of different land use types （agricultural land, industrial land, commercial land, residential land, green space and water area） within different buffer radii （1 to 20 kin）. This method makes up for the lack of consideration of source impact based on the LUR model. Remote sensing-derived variables were significantly correlated with gaseous pollutant concentrations such as SO2 and NO2. R2 values of the multiple linear regression equations for SO2, NO2 and PM10 were 0.78, 0.89 and 0.84, respectively, and the RMSE values were 0.32, 0.18 and 0.21, respectively. Model predictions at validation monitoring sites went well with predictions generally within 15% of measured values. Compared to the relationship between dependent variables and simple variables （such as traffic variables or meteorological condition variables）, the relationship between dependent variables and integrated variables was more consistent with a linear relationship. Such integration has a discernable influence on both the overall model prediction and health effects assessment on the spatial distribution of air pollution in the city region.展开更多
SO2, NO2, and PM10 are the major outdoor air pollutants in China, and most of the cities in China have regulatory monitoring sites for these three air pollutants. In this study, we developed a land use regression (LUR...SO2, NO2, and PM10 are the major outdoor air pollutants in China, and most of the cities in China have regulatory monitoring sites for these three air pollutants. In this study, we developed a land use regression (LUR) model using regulatory monitoring data to predict the spatial distribution of air pollutant concentrations in Jinan, China. Traffic, land use and census data, and meteorological and physical conditions were included as candidate independent variables, and were tabulated for buffers of varying radii. SO2, NO2, and PM10 concentrations were most highly correlated with the area of industrial land within a buffer of 0.5 km (R2=0.34), the distance from an expressway (R2=0.45), and the area of residential land within a buffer of 1.5 km (R2=0.25), respectively. Three multiple linear regression (MLR) equations were established based on the most significant variables (p<0.05) for SO2, NO2, and PM10, and R2 values obtained were 0.617, 0.640, and 0.600, respectively. An LUR model can be applied to an area with complex terrain. The buffer radii of independent variables for SO2, NO2, and PM10 were chosen to be 0.5, 2, and 1.5 km, respectively based on univariate models. Intercepts of MLR equations can reflect the background concentrations in a certain area, but in this study the intercept values seemed to be higher than background concentrations. Most of the cities in China have a network of regulatory monitoring sites. However, the number of sites has been limited by the level of financial support available. The results of this study could be helpful in promoting the application of LUR models for monitoring pollutants in Chinese cities.展开更多
This article attempts to detail time series characteristics of PM2.5 concentration in Guangzhou(China)from 1 June 2012 to 31 May 2013 based on wavelet analysis tools,and discuss its spatial distribution using geograph...This article attempts to detail time series characteristics of PM2.5 concentration in Guangzhou(China)from 1 June 2012 to 31 May 2013 based on wavelet analysis tools,and discuss its spatial distribution using geographic information system software and a modified land use regression model.In this modified model,an important variable(land use data)is substituted for impervious surface area,which can be obtained conveniently from remote sensing imagery through the linear spectral mixture analysis method.Impervious surface has higher precision than land use data because of its sub-pixel level.Seasonal concentration pattern and day-by-day change feature of PM2.5 in Guangzhou with a micro-perspective are discussed and understood.Results include:(1)the highest concentration of PM2.5 occurs in October and the lowest in July,respectively;(2)average concentration of PM2.5 in winter is higher than in other seasons;and(3)there are two high concentration zones in winter and one zone in spring.展开更多
The intraurban distribution of PM_(2.5)concentration is influenced by various spatial,socioeconomic,and meteorological parameters.This study investigated the influence of 37 parameters on monthly average PM_(2.5)conce...The intraurban distribution of PM_(2.5)concentration is influenced by various spatial,socioeconomic,and meteorological parameters.This study investigated the influence of 37 parameters on monthly average PM_(2.5)concentration at the subdistrict level with Pearson correlation analysis and land-use regression(LUR)using data from a subdistrict-level air pollution monitoring network in Shenzhen,China.Performance of LUR models is evaluated with leave-one-out-cross-validation(LOOCV)and holdout cross-validation(holdout CV).Pearson correlation analysis revealed that Normalized Difference Built-up Index,artificial land fraction,land surface temperature,and point-of-interest(POI)numbers of factories and industrial parks are significantly positively correlated with monthly average PM_(2.5)concentrations,while Normalized Difference Vegetation Index and Green View Factor show significant negative correlations.For the sparse national stations,robust LUR modelling may rely on a priori assumptions in direction of influence during the predictor selection process.The month-bymonth spatial regression shows that RF models for both national stations and all stations show significantly inflated mean values of R^(2)compared with cross-validation results.For MLR models,inflation of both R^(2)and R^(2)CVwas detected when using only national stations and may indicate the restricted ability to predict spatial distribution of PM_(2.5)levels.Inflated within-sample R^(2)also exist in the spatiotemporal LUR models developed with only national stations,although not as significant as spatial LUR models.Our results suggest that a denser subdistrict level air pollutant monitoring network may improve the accuracy and robustness in intraurban spatial/spatiotemporal prediction of PM_(2.5)concentrations.展开更多
文摘The purpose of this study was to establish a method able to accurately estimate the long-term exposure levels of individuals to fine particulate matter (PM2.5) in Jiujiang City (China) by constructing land use regression (LUR) models. Subsequently, the accuracy of models was further verified. PM2.5 concentrations were continuously collected daily from seven monitoring stations for the construction of daily LUR models from September 1 to 14, 2023. The constructed models used PM2.5 concentrations as the dependent variable, while land use, elevation, population density and road length were used as the predictive variables. Subsequently, twenty volunteers were invited to participate, with their daily PM2.5 exposure estimated based on their work address and home address, allowing their average exposure levels to be calculated. Furthermore, volunteers wore portable PM2.5 detectors continuously for a 14-day period and the average measured PM2.5 level was used as a comparative standard. Results showed that the adjusted R2 values for the 14 daily models ranged from 0.85 to 0.94, with the R2 values generated from leave-one-out-cross-validation tests all greater than 0.61, indicating good prediction accuracy. No significant differences were observed in the measurement accuracy of the LUR modeling method and measurements using a portable PM2.5 detector (p > 0.05). This study aimed to develop a novel method for the accurate and convenient measurement of individual long-term PM2.5 exposure levels for epidemiological studies in urban environments comparable to that of Jiujiang city.
基金supported by the Special Environmental Research Funds for Public Welfare (No. 200709048,200909005)the National Natural Science Foundation of China (No. 20677030)
文摘Land use regression （LUR） model was employed to predict the spatial concentration distribution of NO2 and PM10 in the Tianjin region based on the environmental air quality monitoring data. Four multiple linear regression （MLR） equations were established based on the most significant variables for NO2 in heating season （R2 = 0.74）, and non-heating season （R2 = 0.61） in the whole study area; and PM10 in heating season （R2 = 0.72）, and non-heating season （R2 = 0.49）. Maps of spatial concentration distribution for NO2 and PM10 were obtained based on the MLR equations （resolution is 10 krn）. Intercepts of MLR equations were 0.050 （NOz, heating season）, 0.035 （NO2, non-heating season）, 0.068 （PM10, heating season）, and 0.092 （PM10, non-beating season） in the whole study area. In the central area of Tianjin region, the intercepts were 0.042 （NO2, heating season）, 0.043 （NO2, non-heating season）, 0.087 （PM10, heating season）, and 0.096 （PMl0, non-heating season）. These intercept values might imply an area＇s background concentrations. Predicted result derived from LUR model in the central area was better than that in the whole study area. Rz values increased 0.09 （heating season） and 0.18 （non-heating season） for NO2, and 0.08 （heating season） and 0.04 （non-heating season） for PMl0. In terms of R2, LUR model performed more effectively in heating season than non-heating season in the study area and gave a better result for NOz compared with PM10.
基金supported by the Special Environmental Research Funds for Public Welfare (No. 200909005)the National Natural Science Foundation of China (No.20677030)the Doctor Funds of Tianjin Normal University (No. 52XB1110)
文摘Advancing the understanding of the spatial aspects of air pollution in the city regional environment is an area where improved methods can be of great benefit to exposure assessment and policy support. We created land use regression （LUR） models for SO2, NO2 and PMI0 for Tianjin, China. Traffic volumes, road networks, land use data, population density, meteorological conditions, physical conditions and satellite-derived greenness, brightness and wetness were used for predicting SOa, NO2 and PMt0 concentrations. We incorporated data on industrial point sources to improve LUR model performance. In order to consider the impact of different sources, we calculated the PSIndex, LSIndex and area of different land use types （agricultural land, industrial land, commercial land, residential land, green space and water area） within different buffer radii （1 to 20 kin）. This method makes up for the lack of consideration of source impact based on the LUR model. Remote sensing-derived variables were significantly correlated with gaseous pollutant concentrations such as SO2 and NO2. R2 values of the multiple linear regression equations for SO2, NO2 and PM10 were 0.78, 0.89 and 0.84, respectively, and the RMSE values were 0.32, 0.18 and 0.21, respectively. Model predictions at validation monitoring sites went well with predictions generally within 15% of measured values. Compared to the relationship between dependent variables and simple variables （such as traffic variables or meteorological condition variables）, the relationship between dependent variables and integrated variables was more consistent with a linear relationship. Such integration has a discernable influence on both the overall model prediction and health effects assessment on the spatial distribution of air pollution in the city region.
基金Project supported by the National Natural Science Foundation of China (No. 20677030)the Development Plan of Key National Fun-damental Research (No. 2011CB503801)the Special Research Funds for Science Development in Jinan (No. 200904015), China
文摘SO2, NO2, and PM10 are the major outdoor air pollutants in China, and most of the cities in China have regulatory monitoring sites for these three air pollutants. In this study, we developed a land use regression (LUR) model using regulatory monitoring data to predict the spatial distribution of air pollutant concentrations in Jinan, China. Traffic, land use and census data, and meteorological and physical conditions were included as candidate independent variables, and were tabulated for buffers of varying radii. SO2, NO2, and PM10 concentrations were most highly correlated with the area of industrial land within a buffer of 0.5 km (R2=0.34), the distance from an expressway (R2=0.45), and the area of residential land within a buffer of 1.5 km (R2=0.25), respectively. Three multiple linear regression (MLR) equations were established based on the most significant variables (p<0.05) for SO2, NO2, and PM10, and R2 values obtained were 0.617, 0.640, and 0.600, respectively. An LUR model can be applied to an area with complex terrain. The buffer radii of independent variables for SO2, NO2, and PM10 were chosen to be 0.5, 2, and 1.5 km, respectively based on univariate models. Intercepts of MLR equations can reflect the background concentrations in a certain area, but in this study the intercept values seemed to be higher than background concentrations. Most of the cities in China have a network of regulatory monitoring sites. However, the number of sites has been limited by the level of financial support available. The results of this study could be helpful in promoting the application of LUR models for monitoring pollutants in Chinese cities.
基金This work is supported by the National Nature Science Foundation of China[grant number:41201432],the National Science Foundation of Tibet[grant number:2016ZR-TU-05]the Foundation for Innovative Research for Young Teachers in Higher Educational Institutions of Tibet[grant number:QCZ2016-07].
文摘This article attempts to detail time series characteristics of PM2.5 concentration in Guangzhou(China)from 1 June 2012 to 31 May 2013 based on wavelet analysis tools,and discuss its spatial distribution using geographic information system software and a modified land use regression model.In this modified model,an important variable(land use data)is substituted for impervious surface area,which can be obtained conveniently from remote sensing imagery through the linear spectral mixture analysis method.Impervious surface has higher precision than land use data because of its sub-pixel level.Seasonal concentration pattern and day-by-day change feature of PM2.5 in Guangzhou with a micro-perspective are discussed and understood.Results include:(1)the highest concentration of PM2.5 occurs in October and the lowest in July,respectively;(2)average concentration of PM2.5 in winter is higher than in other seasons;and(3)there are two high concentration zones in winter and one zone in spring.
基金supported by the National Key Research and Development Program of China(No.2016YFC0202206)the National Natural Science Foundation of China(Nos.41875015,42105070 and 42175095)+2 种基金the Key projects of Guangdong Natural Science Foundation,China(No.2018B030311068)Special fund for science and technology innovation strategy of Guangdong Province(International cooperation),China(No.2019A050510021)the Guangdong Basic and Applied Basic Research Foundation(No.2020A1515110278)
文摘The intraurban distribution of PM_(2.5)concentration is influenced by various spatial,socioeconomic,and meteorological parameters.This study investigated the influence of 37 parameters on monthly average PM_(2.5)concentration at the subdistrict level with Pearson correlation analysis and land-use regression(LUR)using data from a subdistrict-level air pollution monitoring network in Shenzhen,China.Performance of LUR models is evaluated with leave-one-out-cross-validation(LOOCV)and holdout cross-validation(holdout CV).Pearson correlation analysis revealed that Normalized Difference Built-up Index,artificial land fraction,land surface temperature,and point-of-interest(POI)numbers of factories and industrial parks are significantly positively correlated with monthly average PM_(2.5)concentrations,while Normalized Difference Vegetation Index and Green View Factor show significant negative correlations.For the sparse national stations,robust LUR modelling may rely on a priori assumptions in direction of influence during the predictor selection process.The month-bymonth spatial regression shows that RF models for both national stations and all stations show significantly inflated mean values of R^(2)compared with cross-validation results.For MLR models,inflation of both R^(2)and R^(2)CVwas detected when using only national stations and may indicate the restricted ability to predict spatial distribution of PM_(2.5)levels.Inflated within-sample R^(2)also exist in the spatiotemporal LUR models developed with only national stations,although not as significant as spatial LUR models.Our results suggest that a denser subdistrict level air pollutant monitoring network may improve the accuracy and robustness in intraurban spatial/spatiotemporal prediction of PM_(2.5)concentrations.
