Historical haze episodes（2013–16） in Guangzhou were examined and classified according to synoptic weather systems.Four types of weather systems were found to be unfavorable, among which ＂foreside of a cold front＂...Historical haze episodes（2013–16） in Guangzhou were examined and classified according to synoptic weather systems.Four types of weather systems were found to be unfavorable, among which ＂foreside of a cold front＂（FC） and ＂sea high pressure＂（SP） were the most frequent（〉 75% of the total）. Targeted case studies were conducted based on an FC-affected event and an SP-affected event with the aim of understanding the characteristics of the contributions of source regions to fine particulate matter（PM（2.5）） in Guangzhou. Four kinds of contributions—namely, emissions outside Guangdong Province（super-region）, emissions from the Pearl River Delta region（PRD region）, emissions from Guangzhou–Foshan–Shenzhen（GFS region）, and emissions from Guangzhou（local）—were investigated using the Weather Research and Forecasting–Community Multiscale Air Quality model. The results showed that the source region contribution differed with different weather systems. SP was a stagnant weather condition, and the source region contribution ratio showed that the local region was a major contributor（37%）, while the PRD region, GFS region and the super-region only contributed 8%, 2.8% and 7%, respectively, to PM（2.5） concentrations. By contrast, FC favored regional transport. The super-region became noticeable,contributing 34.8%, while the local region decreased to 12%. A simple method was proposed to quantify the relative impact of meteorology and emissions. Meteorology had a 35% impact, compared with an impact of-18% for emissions, when comparing the FC-affected event with that of the SP. The results from this study can provide guidance to policymakers for the implementation of effective control strategies.展开更多
Generalized Additive Models(GAMs)are widely employed in ecological research,serving as a powerful tool for ecologists to explore complex nonlinear relationships between a response variable and predictors.Nevertheless,...Generalized Additive Models(GAMs)are widely employed in ecological research,serving as a powerful tool for ecologists to explore complex nonlinear relationships between a response variable and predictors.Nevertheless,evaluating the relative importance of predictors with concurvity(analogous to collinearity)on response variables in GAMs remains a challenge.To address this challenge,we developed an R package named gam.hp.gam.hp calculates individual R^(2) values for predictors,based on the concept of'average shared variance',a method previously introduced for multiple regression and canonical analyses.Through these individual R^(2)s,which add up to the overall R^(2),researchers can evaluate the relative importance of each predictor within GAMs.We illustrate the utility of the gam.hp package by evaluating the relative importance of emission sources and meteorological factors in explaining ozone concentration variability in air quality data from London,UK.We believe that the gam.hp package will improve the interpretation of results obtained from GAMs.展开更多
基金supported by the National Key R&D Program of China:Task 3(Grant No.2016 YFC0202000)Guangzhou Science and Technology Plan(Grant No.201604020028)+3 种基金National Natural Science Foundation of China(Grant No.41775037 and 41475105)Science and Technology Innovative Research Team Plan of Guangdong Meteorological Bureau(Grant No.201704)Guangdong Natural Science FoundationMajor Research Training Project(2015A030308014)a science and technology study project of Guangdong Meteorological Bureau(Grant No.2015Q03)
文摘Historical haze episodes（2013–16） in Guangzhou were examined and classified according to synoptic weather systems.Four types of weather systems were found to be unfavorable, among which ＂foreside of a cold front＂（FC） and ＂sea high pressure＂（SP） were the most frequent（〉 75% of the total）. Targeted case studies were conducted based on an FC-affected event and an SP-affected event with the aim of understanding the characteristics of the contributions of source regions to fine particulate matter（PM（2.5）） in Guangzhou. Four kinds of contributions—namely, emissions outside Guangdong Province（super-region）, emissions from the Pearl River Delta region（PRD region）, emissions from Guangzhou–Foshan–Shenzhen（GFS region）, and emissions from Guangzhou（local）—were investigated using the Weather Research and Forecasting–Community Multiscale Air Quality model. The results showed that the source region contribution differed with different weather systems. SP was a stagnant weather condition, and the source region contribution ratio showed that the local region was a major contributor（37%）, while the PRD region, GFS region and the super-region only contributed 8%, 2.8% and 7%, respectively, to PM（2.5） concentrations. By contrast, FC favored regional transport. The super-region became noticeable,contributing 34.8%, while the local region decreased to 12%. A simple method was proposed to quantify the relative impact of meteorology and emissions. Meteorology had a 35% impact, compared with an impact of-18% for emissions, when comparing the FC-affected event with that of the SP. The results from this study can provide guidance to policymakers for the implementation of effective control strategies.
基金supported by the National Natural Science Foundation of China (32271551)National Key Research and Development Program of China (2023YFF0805803)the Metasequoia funding of Nanjing Forestry University。
文摘Generalized Additive Models(GAMs)are widely employed in ecological research,serving as a powerful tool for ecologists to explore complex nonlinear relationships between a response variable and predictors.Nevertheless,evaluating the relative importance of predictors with concurvity(analogous to collinearity)on response variables in GAMs remains a challenge.To address this challenge,we developed an R package named gam.hp.gam.hp calculates individual R^(2) values for predictors,based on the concept of'average shared variance',a method previously introduced for multiple regression and canonical analyses.Through these individual R^(2)s,which add up to the overall R^(2),researchers can evaluate the relative importance of each predictor within GAMs.We illustrate the utility of the gam.hp package by evaluating the relative importance of emission sources and meteorological factors in explaining ozone concentration variability in air quality data from London,UK.We believe that the gam.hp package will improve the interpretation of results obtained from GAMs.
