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In the present study, we aim to evaluate the delayed and cumulative effect of ozone (O3) exposure on mumps in a megacity with high population density and high humidity. We took Chongqing, a megacity in Southwest China, as the research area and 2013–2017 as the research period. A total of 49,258 confirmed mumps cases were collected from 122 hospitals of Chongqing. We employed the distributed lag nonlinear models with quasi-Poisson link to investigate the relationship between prevalence of mumps and O3 exposure after adjusting for the effects of meteorological conditions. The results show that the effect of O3 exposure on mumps was mainly manifested in the lag of 0–7 days. The single-day ;lag effect was the most obvious on the 4th day, with the relative risk (RR) of mumps occurs of 1.006 (95% CI: 1.003–1.007) per 10 μg/m3 in the O3 exposure. The cumulative RR within 7 days was 1.025 (95% CI: 1.013–1.038). Our results suggest that O3 exposure can increase the risk of mumps infection, which fills the gap of relevant research in mountainous areas with high population density and high humidity.
Wenjun Xie; Han Zhao; Chang Shu; Bin Wang; Wen Zeng; Yu Zhan. Association between ozone exposure and prevalence of mumps: a time-series study in a Megacity of Southwest China. Environmental Science and Pollution Research 2021, 1 -10.
AMA StyleWenjun Xie, Han Zhao, Chang Shu, Bin Wang, Wen Zeng, Yu Zhan. Association between ozone exposure and prevalence of mumps: a time-series study in a Megacity of Southwest China. Environmental Science and Pollution Research. 2021; ():1-10.
Chicago/Turabian StyleWenjun Xie; Han Zhao; Chang Shu; Bin Wang; Wen Zeng; Yu Zhan. 2021. "Association between ozone exposure and prevalence of mumps: a time-series study in a Megacity of Southwest China." Environmental Science and Pollution Research , no. : 1-10.
The association between maternal exposure to gaseous air pollutants and congenital heart defects (CHD) remains unclear. The concentration-response relationship and the time windows of susceptibility to gaseous pollutants may vary by pollutant species and CHD subtypes. We aimed to examine the relationship between maternal exposures to four species of gaseous pollutants (NO2, O3, SO2, and CO) and atrial septal defect (ASD), which is a common subtype of CHD, and to determine the critical time windows of susceptibility for each gaseous pollutant. Among 1,253,633 infants born between October 1, 2013 and December 31, 2016 in China, 1937 newborns were diagnosed with isolated ASD, a prevalence of 1.55‰. Maternal exposures to the gaseous pollutants were estimated by matching the geocoded maternal addresses with the gridded ambient concentrations. The adjusted odds ratios (aOR) between exposures and ASD were quantified by using mixed-effects logistic regression models. We found significantly positive associations between ASD and maternal exposures to NO2, O3, SO2, and CO during entire pregnancy, first-, second-, and third-trimester. However, no statistically significant association was found between maternal exposure to PM2.5, PM2.5-10 and ASD risk (P > 0.05). In the fully adjusted model with respect to average exposure over entire pregnancy, the adjusted odds ratios (aOR) for each 10 μg/m3 increment of NO2, O3, SO2 were 1.33 (95% CI: 1.22–1.45), 1.13 (95% CI: 1.10–1.16), 1.28 (95% CI: 1.20–1.35), respectively; the aOR for each 100 μg/m3 increment of CO was 1.10 (95% CI: 1.06–1.15). The observed concentration-response relationships varied by exposure periods and pollutants, with the strongest association for NO2 during the 1st-8th embryology weeks, for O3 during the third trimester, for SO2 during the second trimester, and for CO without obvious variation. The findings suggest an increased risk of ASD in association with maternal exposures to four common gaseous pollutants. From the perspective of birth defects prevention and ASD risk mitigation, it is critical to reduce maternal exposure to gaseous pollutants especially during the most susceptible time windows.
Fangyuan Yan; Hanmin Liu; Hanyue Zhang; Ling Yi; Yangyang Wu; Changfei Deng; Yang Qiu; Xia Ma; Qi Li; Fumo Yang; Wenli Xu; Jing Tao; Jonathan J. Buonocore; Yu Zhan; Li Dai. Association between maternal exposure to gaseous pollutants and atrial septal defect in China: A nationwide population-based study. Environmental Research 2021, 200, 111472 .
AMA StyleFangyuan Yan, Hanmin Liu, Hanyue Zhang, Ling Yi, Yangyang Wu, Changfei Deng, Yang Qiu, Xia Ma, Qi Li, Fumo Yang, Wenli Xu, Jing Tao, Jonathan J. Buonocore, Yu Zhan, Li Dai. Association between maternal exposure to gaseous pollutants and atrial septal defect in China: A nationwide population-based study. Environmental Research. 2021; 200 ():111472.
Chicago/Turabian StyleFangyuan Yan; Hanmin Liu; Hanyue Zhang; Ling Yi; Yangyang Wu; Changfei Deng; Yang Qiu; Xia Ma; Qi Li; Fumo Yang; Wenli Xu; Jing Tao; Jonathan J. Buonocore; Yu Zhan; Li Dai. 2021. "Association between maternal exposure to gaseous pollutants and atrial septal defect in China: A nationwide population-based study." Environmental Research 200, no. : 111472.
Long-term surface NO2 data are essential for retrospective policy evaluation and chronic human exposure assessment. In the absence of NO2 observations for Mainland China before 2013, training a model with 2013–2018 data to make predictions for 2005–2012 (back-extrapolation) could cause substantial estimation bias due to concept drift. This study aims to correct the estimation bias in order to reconstruct the spatiotemporal distribution of daily surface NO2 levels across China during 2005–2018. On the basis of ground- and satellite-based data, we proposed the robust back-extrapolation with a random forest (RBE-RF) to simulate the surface NO2 through intermediate modeling of the scaling factors. For comparison purposes, we also employed a random forest (Base-RF), as a representative of the commonly used approach, to directly model the surface NO2 levels. The validation against Taiwan’s NO2 observations during 2005–2012 showed that RBE-RF adequately corrected the substantial underestimation by Base-RF. The RMSE decreased from 10.1 to 8.2 µg/m3, 7.1 to 4.3 µg/m3, and 6.1 to 2.9 µg/m3 in predicting daily, monthly, and annual levels, respectively. For North China with the most severe pollution, the population-weighted NO2 ([NO2]pw) during 2005–2012 was estimated as 40.2 and 50.9 µg/m3 by Base-RF and RBE-RF, respectively, i.e., 21.0% difference. While both models predicted that the national annual [NO2]pw increased during 2005–2011 and then decreased, the interannual trends were underestimated by >50.2% by Base-RF relative to RBE-RF. During 2005–2018, the nationwide population that lived in the areas with NO2 > 40 µg/m3 were estimated as 259 and 460 million by Base-RF and RBE-RF, respectively. With RBE-RF, we corrected the estimation bias in back-extrapolation and obtained a full-coverage dataset of daily surface NO2 across China during 2005–2018, which is valuable for environmental management and epidemiological research.
Yangyang Wu; Baofeng Di; Yuzhou Luo; Michael L. Grieneisen; Wen Zeng; Shifu Zhang; Xunfei Deng; Yulei Tang; Guangming Shi; Fumo Yang; Yu Zhan. A robust approach to deriving long-term daily surface NO2 levels across China: Correction to substantial estimation bias in back-extrapolation. Environment International 2021, 154, 106576 .
AMA StyleYangyang Wu, Baofeng Di, Yuzhou Luo, Michael L. Grieneisen, Wen Zeng, Shifu Zhang, Xunfei Deng, Yulei Tang, Guangming Shi, Fumo Yang, Yu Zhan. A robust approach to deriving long-term daily surface NO2 levels across China: Correction to substantial estimation bias in back-extrapolation. Environment International. 2021; 154 ():106576.
Chicago/Turabian StyleYangyang Wu; Baofeng Di; Yuzhou Luo; Michael L. Grieneisen; Wen Zeng; Shifu Zhang; Xunfei Deng; Yulei Tang; Guangming Shi; Fumo Yang; Yu Zhan. 2021. "A robust approach to deriving long-term daily surface NO2 levels across China: Correction to substantial estimation bias in back-extrapolation." Environment International 154, no. : 106576.
To evaluate the impact of air pollution exposure on semen quality parameters during COVID-19 outbreak in China, and to identify potential windows of susceptibility for semen quality. A retrospective observational study was carried out on 1991 semen samples collected between November 23, 2019 and July 23, 2020 (a period covering COVID-19 lock-down in China) from 781 sperm donor candidates at University-affiliated Sichuan Provincial Human Sperm Bank. Multivariate mixed-effects regression models were constructed to investigate the relationship between pollution exposure, windows of susceptibility, and semen quality, while controlling for biographic and meteorologic confounders. The results indicated multiple windows of susceptibility for semen quality, especially sperm motility, due to ambient pollution exposure. Exposure to particulate matters (PM2.5 and PM10), O3 and NO2 during late stages of spermatogenesis appeared to have weak but positive association with semen quality. Exposure to CO late in sperm development appeared to have inverse relationship with sperm movement parameters. Exposure to SO2 appeared to influence semen quality throughout spermatogenesis. Potential windows of susceptibility for semen quality varied depending on air pollutants. Sperm motility was sensitive to pollution exposure. Findings from current study further elucidate the importance of sensitive periods during spermatogenesis and provide new evidence for the determinants of male fertility.
Tingting Yang; Li Deng; Boyu Sun; Shifu Zhang; Yang Xian; Xiao Xiao; Yu Zhan; Kehui Xu; Johnathan J. Buonocore; Ya Tang; Fuping Li; Yang Qiu. Semen quality and windows of susceptibility: A case study during COVID-19 outbreak in China. Environmental Research 2021, 197, 111085 .
AMA StyleTingting Yang, Li Deng, Boyu Sun, Shifu Zhang, Yang Xian, Xiao Xiao, Yu Zhan, Kehui Xu, Johnathan J. Buonocore, Ya Tang, Fuping Li, Yang Qiu. Semen quality and windows of susceptibility: A case study during COVID-19 outbreak in China. Environmental Research. 2021; 197 ():111085.
Chicago/Turabian StyleTingting Yang; Li Deng; Boyu Sun; Shifu Zhang; Yang Xian; Xiao Xiao; Yu Zhan; Kehui Xu; Johnathan J. Buonocore; Ya Tang; Fuping Li; Yang Qiu. 2021. "Semen quality and windows of susceptibility: A case study during COVID-19 outbreak in China." Environmental Research 197, no. : 111085.
Exposure to outdoor fine particulate matter (PM2.5)-bound polycyclic aromatic hydrocarbons (PAHs) is linked to reproductive dysfunction. However, it is unclear which component of PAHs is responsible for the adverse outcomes. In the Male Reproductive Health in Chongqing College Students (MARHCS) cohort study, we measured the exposure levels of 16 PAHs by collecting air PM2.5 particles and assessed eight PAHs metabolites from four parent PAHs, including naphthalene, fluorene, phenanthrene, and pyrene in urine samples. We investigated compositional profiles and variation characteristics for 16 PAHs in PM2.5, and then assessed the association between PAHs exposure and semen routine parameters, sperm chromatin structure, and serum hormone levels in 1452 samples. The results showed that naphthalene (95% CI: −17.989, −8.101), chrysene (95% CI: −64.894, −47.575), benzo[a]anthracene (95% CI: −63.227, −45.936) and all the high molecular weight (HMW) PAHs in PM2.5 were negatively associated with sperm normal morphology. Most of the low molecular weight (LMW) PAHs, such as acenaphthylene, fluorene, phenanthrene, fluoranthene, pyrene, chrysene, benzo[a]anthracene, ∑LMW PAHs and ∑16 PAHs, were correlated with increased sperm motility (all corrected P < 0.05). On the other hand, sperm normal morphology was all negatively associated with urinary metabolites of ∑OH-Nap (95% CI: −5.611, −0.536), ∑OH-Phe (95% CI: −5.741, −0.957), and ∑OH-PAHs (95% CI: −5.274, −0.361). Urinary concentrations of ∑OH-PAHs were found to be negatively associated with sperm high DNA stainability (HDS) (P = 0.023), while ∑OH-Phe were negatively associated with serum testosterone level and sperm HDS (P = 0.004). Spearman correlation analysis showed that except for the urinary OH-Nap metabolites, the rest of the urinary OH-PAHs metabolites were negatively correlated with their parent PAHs in air. The results of this study suggest that various PAHs’ components may affect reproductive parameters differently. Inhalation of PAHs in air, especially HMW PAHs, may be a potential risk factor for male reproductive health.
Qing Chen; Furong Wang; Huan Yang; Xiaogang Wang; Aihua Zhang; Xi Ling; Lianbing Li; Peng Zou; Lei Sun; Linping Huang; Hongqiang Chen; Lin Ao; Jinyi Liu; Jia Cao; Niya Zhou. Exposure to fine particulate matter-bound polycyclic aromatic hydrocarbons, male semen quality, and reproductive hormones: The MARCHS study. Environmental Pollution 2021, 280, 116883 .
AMA StyleQing Chen, Furong Wang, Huan Yang, Xiaogang Wang, Aihua Zhang, Xi Ling, Lianbing Li, Peng Zou, Lei Sun, Linping Huang, Hongqiang Chen, Lin Ao, Jinyi Liu, Jia Cao, Niya Zhou. Exposure to fine particulate matter-bound polycyclic aromatic hydrocarbons, male semen quality, and reproductive hormones: The MARCHS study. Environmental Pollution. 2021; 280 ():116883.
Chicago/Turabian StyleQing Chen; Furong Wang; Huan Yang; Xiaogang Wang; Aihua Zhang; Xi Ling; Lianbing Li; Peng Zou; Lei Sun; Linping Huang; Hongqiang Chen; Lin Ao; Jinyi Liu; Jia Cao; Niya Zhou. 2021. "Exposure to fine particulate matter-bound polycyclic aromatic hydrocarbons, male semen quality, and reproductive hormones: The MARCHS study." Environmental Pollution 280, no. : 116883.
In September 2018, China’s air quality monitoring protocol was amended from the standard conditions to actual conditions for particulate matter and to reference conditions for gaseous pollutants. Due to the amendment, the reported concentrations of the gaseous pollutants decreased by a constant rate of 8.4%, and the averages of PM2.5 (particulate matter that has an aerodynamic diameter of 2.5 microns or smaller) reported during September 2017 and August 2018 decreased by 7.9 ± 6.1% at 99% of the monitoring stations. Comparing the periods before and after the amendment, the 12-month PM2.5 concentrations at 17.2% of the stations actually increased despite appearing to decrease if the amendments were not considered. We reviewed 370 papers published in 2020 that utilized this air quality dataset, and 21% of these papers used the data before and after the amendment without explicitly stating whether or how conversions were conducted. It is urgent to widely broadcast the protocol amendment to ensure proper use of this extensively cited dataset.
Lei Jin; Bin Wang; Guangming Shi; Barnabas Seyler; Xue Qiao; Xunfei Deng; Xia Jiang; Fumo Yang; Yu Zhan. Impact of China’s Recent Amendments to Air Quality Monitoring Protocol on Reported Trends. Atmosphere 2020, 11, 1199 .
AMA StyleLei Jin, Bin Wang, Guangming Shi, Barnabas Seyler, Xue Qiao, Xunfei Deng, Xia Jiang, Fumo Yang, Yu Zhan. Impact of China’s Recent Amendments to Air Quality Monitoring Protocol on Reported Trends. Atmosphere. 2020; 11 (11):1199.
Chicago/Turabian StyleLei Jin; Bin Wang; Guangming Shi; Barnabas Seyler; Xue Qiao; Xunfei Deng; Xia Jiang; Fumo Yang; Yu Zhan. 2020. "Impact of China’s Recent Amendments to Air Quality Monitoring Protocol on Reported Trends." Atmosphere 11, no. 11: 1199.
Conventional interpolation methods, such as spatial averaging, nearest neighbor, inverse distance weight and ordinary kriging (OK); for estimating the spatial distribution of ground-level particulate matter (PM) data, do not account for the wind direction for estimating the spatial distribution of PM2.5. In this work, an interpolation algorithm, Win-OK accounting for the wind direction, is developed. In contrast to ordinary kriging where all locations (irrespective of the wind direction) in the vicinity of a site is considered, the new algorithm (Win-OK) predicts the value at a certain location based on the measured values at locations upwind as determined by the wind direction. This new methodology, Win-OK is validated by applying it to analyze the hourly spatial distribution of ground-level PM2.5 concentrations during Chinese New Year and Chinese National Day in 2017 in Xinxiang city, China. The performance of OK and Win-OK are compared by using them to build PM2.5 concentration heat-maps. A “leave-one-out” cross validation methodology is used to calculate the root-mean-square error (RMSE) and standard deviation for evaluating both algorithms. The results show that OK sometimes gives an extremely high RMSE value using a Gaussian semi-variance model, and the standard deviation significantly deviates from the measured values. Win-OK was found to more accurately predict the PM2.5 spatial distribution in a specific sector. The performance of Win-OK is more stable than OK as established by comparing the calculated RMSE and standard deviation from predictions of both algorithms. Win-OK with a spherical semi-variance model is the most accurate method investigated here for deriving the spatial distribution of ground-level PM2.5. The new algorithm developed here could improve the prediction accuracy of PM2.5 spatial distribution by considering the effect of wind direction.
Huang Zhang; Yu Zhan; Jiayu Li; Chun-Ying Chao; Qianfeng Liu; Chunying Wang; Shuangqing Jia; Lin Ma; Pratim Biswas. Using Kriging incorporated with wind direction to investigate ground-level PM2.5 concentration. Science of The Total Environment 2020, 751, 141813 .
AMA StyleHuang Zhang, Yu Zhan, Jiayu Li, Chun-Ying Chao, Qianfeng Liu, Chunying Wang, Shuangqing Jia, Lin Ma, Pratim Biswas. Using Kriging incorporated with wind direction to investigate ground-level PM2.5 concentration. Science of The Total Environment. 2020; 751 ():141813.
Chicago/Turabian StyleHuang Zhang; Yu Zhan; Jiayu Li; Chun-Ying Chao; Qianfeng Liu; Chunying Wang; Shuangqing Jia; Lin Ma; Pratim Biswas. 2020. "Using Kriging incorporated with wind direction to investigate ground-level PM2.5 concentration." Science of The Total Environment 751, no. : 141813.
While accumulating evidence shows that air pollution exposure is an important risk factor to influenza prevalence, their association has been inadequately investigated in mountainous regions with dense populations and high humidity. We aim to estimate the association and exposure-outcome effects between exposure to nitrogen dioxide (NO2) and influenza prevalence in a mountainous region with a dense population and high humidity. We investigated 14,993 patients with confirmed influenza cases from January 2013 to December 2017 in Chongqing, a mountainous city in southwest China. We developed distributed lag non-linear models with quasi-Poisson link to take into account the lag and non-linear effects of NO2 exposure on influenza prevalence. We estimated that the cumulative effect of a 10 μg/m3 increase in NO2 with seven-day lag (i.e., summing all the contributions up to seven days) corresponded to relative risk of 1.24 (95% CI: 1.17–1.31) in daily influenza prevalence. Comparing to annual mean of the World Health Organization air quality guidelines of 40 μg/m3 for NO2, we estimated that 14.01% (95% CI: 10.69–17.08%) of the influenza cases were attributable to excessive NO2 exposure. Our results suggest that NO2 exposure could worsen the risk of influenza infection in this mountainous city, filling the gap of relevant researches in densely populated and mountainous cities. Our findings provide evidence for developing influenza surveillance and early warning systems.
Wen Zeng; Han Zhao; Rui Liu; Wei Yan; Yang Qiu; Fumo Yang; Chang Shu; Yu Zhan. Association between NO2 cumulative exposure and influenza prevalence in mountainous regions: A case study from southwest China. Environmental Research 2020, 189, 109926 -109926.
AMA StyleWen Zeng, Han Zhao, Rui Liu, Wei Yan, Yang Qiu, Fumo Yang, Chang Shu, Yu Zhan. Association between NO2 cumulative exposure and influenza prevalence in mountainous regions: A case study from southwest China. Environmental Research. 2020; 189 ():109926-109926.
Chicago/Turabian StyleWen Zeng; Han Zhao; Rui Liu; Wei Yan; Yang Qiu; Fumo Yang; Chang Shu; Yu Zhan. 2020. "Association between NO2 cumulative exposure and influenza prevalence in mountainous regions: A case study from southwest China." Environmental Research 189, no. : 109926-109926.
Yu Zhan; Jhoon Kim. Editorial: special issue on “air quality monitoring, assessment, & forecasting using GIScience and remote sensing”. GIScience & Remote Sensing 2020, 57, 157 -158.
AMA StyleYu Zhan, Jhoon Kim. Editorial: special issue on “air quality monitoring, assessment, & forecasting using GIScience and remote sensing”. GIScience & Remote Sensing. 2020; 57 (2):157-158.
Chicago/Turabian StyleYu Zhan; Jhoon Kim. 2020. "Editorial: special issue on “air quality monitoring, assessment, & forecasting using GIScience and remote sensing”." GIScience & Remote Sensing 57, no. 2: 157-158.
Debris flow susceptibility mapping is considered to be useful for hazard prevention and mitigation. As a frequent debris flow area, many hazardous events have occurred annually and caused a lot of damage in the Sichuan Province, China. Therefore, this study attempted to evaluate and compare the performance of four state-of-the-art machine-learning methods, namely Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF), and Boosted Regression Trees (BRT), for debris flow susceptibility mapping in this region. Four models were constructed based on the debris flow inventory and a range of causal factors. A variety of datasets was obtained through the combined application of remote sensing (RS) and geographic information system (GIS). The mean altitude, altitude difference, aridity index, and groove gradient played the most important role in the assessment. The performance of these modes was evaluated using predictive accuracy (ACC) and the area under the receiver operating characteristic curve (AUC). The results of this study showed that all four models were capable of producing accurate and robust debris flow susceptibility maps (ACC and AUC values were well above 0.75 and 0.80 separately). With an excellent spatial prediction capability and strong robustness, the BRT model (ACC = 0.781, AUC = 0.852) outperformed other models and was the ideal choice. Our results also exhibited the importance of selecting suitable mapping units and optimal predictors. Furthermore, the debris flow susceptibility maps of the Sichuan Province were produced, which can provide helpful data for assessing and mitigating debris flow hazards.
Ke Xiong; Basanta Raj Adhikari; Constantine A. Stamatopoulos; Yu Zhan; Shaolin Wu; Zhongtao Dong; Baofeng Di. Comparison of Different Machine Learning Methods for Debris Flow Susceptibility Mapping: A Case Study in the Sichuan Province, China. Remote Sensing 2020, 12, 295 .
AMA StyleKe Xiong, Basanta Raj Adhikari, Constantine A. Stamatopoulos, Yu Zhan, Shaolin Wu, Zhongtao Dong, Baofeng Di. Comparison of Different Machine Learning Methods for Debris Flow Susceptibility Mapping: A Case Study in the Sichuan Province, China. Remote Sensing. 2020; 12 (2):295.
Chicago/Turabian StyleKe Xiong; Basanta Raj Adhikari; Constantine A. Stamatopoulos; Yu Zhan; Shaolin Wu; Zhongtao Dong; Baofeng Di. 2020. "Comparison of Different Machine Learning Methods for Debris Flow Susceptibility Mapping: A Case Study in the Sichuan Province, China." Remote Sensing 12, no. 2: 295.
Adequate soil total nitrogen (TN) is critical to crop productivity, and soil carbon-to-nitrogen ratio (C/N) is an important indicator of soil fertility. Accurate knowledge of soil TN content and C/N ratio is essential for precision agriculture and soil biogeochemical modeling. This study aims to characterize the spatial and temporal trends of soil TN level and C/N ratio for cropland soil across Zhejiang province of East China. A total of 29,927 topsoil (0–20 cm) samples were collected during 2007–2008 for analyzing soil properties, on the basis of which we mapped the soil TN content at a 250 m resolution using a random forest model (RF). With 22 predictors covering soil properties, fertilization, meteorology, locations, and population density, the RF model showed good predictive performance with cross-validation R2 = 0.65 and RMSE = 0.43 g kg−1. The soil pH, cation exchange capacity (CEC), and fertilization played important roles in determining the spatial variations of soil TN and C/N ratio. The overall level of cropland soil TN within the study area was estimated to be 1.75 ± 0.43 g kg−1 (μ ± σ), varying from 0.25 to 3.64 g kg−1. By comparing our estimates with the previous extensive survey for 1979–1985, we found that the soil TN has slightly increase trend and the soil C/N ratio slightly decreased, which was attributed to intensified N fertilization in the last decade. This work provides up-to-date knowledge of soil TN content and soil C/N ratio in Zhejiang province of East China, indicating the importance of updating soil property databases for better regional cropland management.
Xunfei Deng; Wanzhu Ma; Zhouqiao Ren; Minghua Zhang; Michael L. Grieneisen; Xiaojia Chen; Xufeng Fei; Fangjin Qin; Yu Zhan; Xiaonan Lv. Spatial and temporal trends of soil total nitrogen and C/N ratio for croplands of East China. Geoderma 2019, 361, 114035 .
AMA StyleXunfei Deng, Wanzhu Ma, Zhouqiao Ren, Minghua Zhang, Michael L. Grieneisen, Xiaojia Chen, Xufeng Fei, Fangjin Qin, Yu Zhan, Xiaonan Lv. Spatial and temporal trends of soil total nitrogen and C/N ratio for croplands of East China. Geoderma. 2019; 361 ():114035.
Chicago/Turabian StyleXunfei Deng; Wanzhu Ma; Zhouqiao Ren; Minghua Zhang; Michael L. Grieneisen; Xiaojia Chen; Xufeng Fei; Fangjin Qin; Yu Zhan; Xiaonan Lv. 2019. "Spatial and temporal trends of soil total nitrogen and C/N ratio for croplands of East China." Geoderma 361, no. : 114035.
Given its relatively long lifetime in the troposphere, carbon monoxide (CO) is commonly employed as a tracer for characterizing airborne pollutant distributions. The present study aims to estimate the spatiotemporal distributions of ground-level CO concentrations across China during 2013–2016. We refined the random-forest–spatiotemporal kriging (RF–STK) model to simulate the daily CO concentrations on a 0.1∘ grid based on the extensive CO monitoring data and the Measurements of Pollution in the Troposphere CO retrievals (MOPITT CO). The RF–STK model alleviated the negative effects of sampling bias and variance heterogeneity on the model training, with cross-validation R2 of 0.51 and 0.71 for predicting the daily and multiyear average CO concentrations, respectively. The national population-weighted average CO concentrations were predicted to be 0.99±0.30 mg m−3 (μ±σ) and showed decreasing trends over all regions of China at a rate of -0.021±0.004 mg m−3 yr−1. The CO pollution was more severe in North China (1.19±0.30 mg m−3), and the predicted patterns were generally consistent with MOPITT CO. The hotspots in the central Tibetan Plateau where the CO concentrations were underestimated by MOPITT CO were apparent in the RF–STK predictions. This comprehensive dataset of ground-level CO concentrations is valuable for air quality management in China.
Dongren Liu; Baofeng Di; Yuzhou Luo; Xunfei Deng; Hanyue Zhang; Fumo Yang; Michael L. Grieneisen; Yu Zhan. Estimating ground-level CO concentrations across China based on the national monitoring network and MOPITT: potentially overlooked CO hotspots in the Tibetan Plateau. Atmospheric Chemistry and Physics 2019, 19, 12413 -12430.
AMA StyleDongren Liu, Baofeng Di, Yuzhou Luo, Xunfei Deng, Hanyue Zhang, Fumo Yang, Michael L. Grieneisen, Yu Zhan. Estimating ground-level CO concentrations across China based on the national monitoring network and MOPITT: potentially overlooked CO hotspots in the Tibetan Plateau. Atmospheric Chemistry and Physics. 2019; 19 (19):12413-12430.
Chicago/Turabian StyleDongren Liu; Baofeng Di; Yuzhou Luo; Xunfei Deng; Hanyue Zhang; Fumo Yang; Michael L. Grieneisen; Yu Zhan. 2019. "Estimating ground-level CO concentrations across China based on the national monitoring network and MOPITT: potentially overlooked CO hotspots in the Tibetan Plateau." Atmospheric Chemistry and Physics 19, no. 19: 12413-12430.
Multiyear spatiotemporal distributions of daily ambient sulfur dioxide (SO2) are essential for evaluating management effectiveness and assessing human health risk. In this study, we estimate the daily SO2 levels across China on 0.1o grid from 2013 to 2016 by assimilating satellite- and ground-based SO2 observations using the random-forest spatiotemporal kriging (RF-STK) model. The cross-validation R2 is 0.64 and 0.81 for predicting the daily and multiyear averages, respectively. The multiyear population-weighted average of SO2 for China is 28.1 ± 14.0 μg/m3, and the severest SO2 pollution occurs in the northern China (45.1 ± 14.7 μg/m3). The SO2 concentration shows a strong seasonality, i.e., highest in winter (41.6 ± 26.4 μg/m3) and lowest in summer (19.6 ± 8.3 μg/m3). During 2013–2016, the annual SO2 decreases from 34.4 ± 18.2 to 22.7 ± 11.1 μg/m3, and the population% exposed for more than 100 nonattainment days (SO2 > 20 μg/m3) drops from 86% to 48%. While the seasonality of SO2 is mainly determined by the meteorological variation, the substantial decrease attributes to the reduced emissions such as from coal consumption. The effectiveness of SO2 emission reduction varies widely in different prefectures of China. In Shandong province, the SO2 concentration decreases by −45% while the coal consumption increases by 9%. In Shanxi province, the SO2 concentration decreases by −15% while the coal consumption decreases by −3%. The contrasting effectiveness between these two provinces is associated with the much fewer waste gas disposal facilities in Shanxi than Shandong. Stricter regulation is required to further lower the SO2 concentration in order to protect the public health, especially in the northern China.
Hanyue Zhang; Baofeng Di; Dongren Liu; Jierui Li; Yu Zhan. Spatiotemporal distributions of ambient SO2 across China based on satellite retrievals and ground observations: Substantial decrease in human exposure during 2013–2016. Environmental Research 2019, 179, 108795 .
AMA StyleHanyue Zhang, Baofeng Di, Dongren Liu, Jierui Li, Yu Zhan. Spatiotemporal distributions of ambient SO2 across China based on satellite retrievals and ground observations: Substantial decrease in human exposure during 2013–2016. Environmental Research. 2019; 179 ():108795.
Chicago/Turabian StyleHanyue Zhang; Baofeng Di; Dongren Liu; Jierui Li; Yu Zhan. 2019. "Spatiotemporal distributions of ambient SO2 across China based on satellite retrievals and ground observations: Substantial decrease in human exposure during 2013–2016." Environmental Research 179, no. : 108795.
Jierui Li; Hanyue Zhang; Yuzhou Luo; Xunfei Deng; Michael L. Grieneisen; Fumo Yang; Baofeng Di; Yu Zhan. Stepwise genetic algorithm for adaptive management: Application to air quality monitoring network optimization. Atmospheric Environment 2019, 215, 1 .
AMA StyleJierui Li, Hanyue Zhang, Yuzhou Luo, Xunfei Deng, Michael L. Grieneisen, Fumo Yang, Baofeng Di, Yu Zhan. Stepwise genetic algorithm for adaptive management: Application to air quality monitoring network optimization. Atmospheric Environment. 2019; 215 ():1.
Chicago/Turabian StyleJierui Li; Hanyue Zhang; Yuzhou Luo; Xunfei Deng; Michael L. Grieneisen; Fumo Yang; Baofeng Di; Yu Zhan. 2019. "Stepwise genetic algorithm for adaptive management: Application to air quality monitoring network optimization." Atmospheric Environment 215, no. : 1.
The aerosol optical depth (AOD) data from the Geostationary Ocean Color Imager (GOCI) and the Himawari-8 are valuable for deriving hourly ambient PM2.5 concentrations for assessing acute human exposure in East Asia. This study aims to comparatively evaluate the performance of these two AOD datasets for estimating the hourly PM2.5 on a 1-km grid by using the nonparametric approach with two random-forest submodels. The full-coverage AOD dataset was generated with the first submodel, followed by the PM2.5 estimation using the second submodel. For the Yangtze River Delta (YRD) in 2017, the validation R2 of filling AOD gaps in the GOCI and Himawari-8 was 0.992 and 0.978, respectively. Estimating the hourly PM2.5 concentrations by using the GOCI and Himawari-8 had similar performance, with the cross-validation R2 of 0.860 and 0.862, respectively. Because the PM2.5 predictions based on these two AOD datasets were almost identical, they were fused with the inverse-variance-weighting method to analyze the spatiotemporal patterns of PM2.5. The annual average hourly PM2.5 across YRD was the highest around 08:00 (45.9 μg/m3) and the lowest around 16:00 (39.0 μg/m3). The cumulative acute exposure assessment shows that approximately 21% of the YRD population was exposed to ambient PM2.5>250 μg/m3 for more than 10 h during 2017. This study demonstrates that the GOCI and Himawari-8 datasets are equally adequate to estimate 24-h full-coverage PM2.5 concentrations for air quality management and human health risk assessments.
Die Tang; Dongren Liu; Yulei Tang; Barnabas Seyler; Xunfei Deng; Yu Zhan. Comparison of GOCI and Himawari-8 aerosol optical depth for deriving full-coverage hourly PM2.5 across the Yangtze River Delta. Atmospheric Environment 2019, 217, 116973 .
AMA StyleDie Tang, Dongren Liu, Yulei Tang, Barnabas Seyler, Xunfei Deng, Yu Zhan. Comparison of GOCI and Himawari-8 aerosol optical depth for deriving full-coverage hourly PM2.5 across the Yangtze River Delta. Atmospheric Environment. 2019; 217 ():116973.
Chicago/Turabian StyleDie Tang; Dongren Liu; Yulei Tang; Barnabas Seyler; Xunfei Deng; Yu Zhan. 2019. "Comparison of GOCI and Himawari-8 aerosol optical depth for deriving full-coverage hourly PM2.5 across the Yangtze River Delta." Atmospheric Environment 217, no. : 116973.
A gradient boosting machine (GBM) was developed to model the susceptibility of debris flow in Sichuan, Southwest China for risk management. A total of 3839 events of debris flow during 1949–2017 were compiled from the Sichuan Geo-Environment Monitoring program, field surveys, and satellite imagery interpretation. In the cross-validation, the GBM showed better performance, with the prediction accuracy of 82.0% and area under curve of 0.88, than the benchmark models, including the Logistic Regression, the K-Nearest Neighbor, the Support Vector Machine, and the Artificial Neural Network. The elevation range, precipitation, and aridity index played the most important role in determining the susceptibility. In addition, the water erosion intensity, road construction, channel gradient, and human settlement sites also largely contributed to the formation of debris flow. The susceptibility map produced by the GBM shows that the spatial distributions of high-susceptibility watersheds were highly coupled with the locations of the topographical extreme belt, fault zone, seismic belt, and dry valleys. This study provides critical information for risk mitigating and prevention of debris flow.
Baofeng Di; Hanyue Zhang; Yongyao Liu; Jierui Li; Ningsheng Chen; Constantine A. Stamatopoulos; Yuzhou Luo; Yu Zhan. Assessing Susceptibility of Debris Flow in Southwest China Using Gradient Boosting Machine. Scientific Reports 2019, 9, 1 -12.
AMA StyleBaofeng Di, Hanyue Zhang, Yongyao Liu, Jierui Li, Ningsheng Chen, Constantine A. Stamatopoulos, Yuzhou Luo, Yu Zhan. Assessing Susceptibility of Debris Flow in Southwest China Using Gradient Boosting Machine. Scientific Reports. 2019; 9 (1):1-12.
Chicago/Turabian StyleBaofeng Di; Hanyue Zhang; Yongyao Liu; Jierui Li; Ningsheng Chen; Constantine A. Stamatopoulos; Yuzhou Luo; Yu Zhan. 2019. "Assessing Susceptibility of Debris Flow in Southwest China Using Gradient Boosting Machine." Scientific Reports 9, no. 1: 1-12.
Yu Zhan. Reply to RC1. 2019, 1 .
AMA StyleYu Zhan. Reply to RC1. . 2019; ():1.
Chicago/Turabian StyleYu Zhan. 2019. "Reply to RC1." , no. : 1.
Yu Zhan. Reply to RC2. 2019, 1 .
AMA StyleYu Zhan. Reply to RC2. . 2019; ():1.
Chicago/Turabian StyleYu Zhan. 2019. "Reply to RC2." , no. : 1.
Phthalate esters (PAEs), typical pollutants widely used as plasticizers, are ubiquitous in various indoor and outdoor environments. PAEs exist in both gas and particle phases, posing risks to human health. In the present study, we chose four typical kinds of indoor and outdoor environments with the longest average human residence times to assess the human exposure in Hangzhou, including newly decorated residences, ordinary residences, offices and outdoor air. In order to analyze the pollution levels and characteristics of 15 gas- and particle-phase PAEs in indoor and outdoor environments, air and particulate samples were collected simultaneously. The total PAEs concentrations in the four types of environments were 25,396, 25,466.8, 15,388.8 and 3616.2 ng/m3, respectively. DEHP and DEP were the most abundant, and DMPP was at the lowest level. Distinct variations in the distributions of indoor/outdoor, gas/particle-phase and different molecular weights of PAEs were observed, showing that indoor environments were the main sources of PAEs pollution. While most PAEs tended to exsit in indoor sites and gas-phase, the high-molecular-weight chemicals tended to exist in the particle-phase and were mainly found in PM2.5. PAEs were more likely adsorbed by small particles, especially for the indoor environments. There existed a good correlation between the particle matter concentrations and the PAEs levels. In addition, neither temperature nor humidity had obvious effects on the distributions of the PAEs concentrations.
Xingzi Ouyang; Meng Xia; Xueyou Shen; Yu Zhan. Pollution characteristics of 15 gas- and particle-phase phthalates in indoor and outdoor air in Hangzhou. Journal of Environmental Sciences 2019, 86, 107 -119.
AMA StyleXingzi Ouyang, Meng Xia, Xueyou Shen, Yu Zhan. Pollution characteristics of 15 gas- and particle-phase phthalates in indoor and outdoor air in Hangzhou. Journal of Environmental Sciences. 2019; 86 ():107-119.
Chicago/Turabian StyleXingzi Ouyang; Meng Xia; Xueyou Shen; Yu Zhan. 2019. "Pollution characteristics of 15 gas- and particle-phase phthalates in indoor and outdoor air in Hangzhou." Journal of Environmental Sciences 86, no. : 107-119.
Given its relatively long lifetime in the troposphere, carbon monoxide (CO) is commonly employed as a tracer for characterizing airborne pollutant distributions. The present study aims to estimate the spatiotemporal distributions of ground-level CO concentrations across China during 2013–2016. A refined random-forest-spatiotemporal-kriging model (RF-STK) is developed to simulate daily gridded CO concentrations (0.1° grid with 98 341 cells) based on the extensive CO monitoring data and the Measurements of Pollution in the Troposphere CO retrievals (MOPITT-CO). The refined RF-STK model alleviates the negative effects of sampling bias and variance heterogeneity on the model training, resulting in cross-validation R2 of 0.51 and 0.71 for predicting daily and spatial CO concentrations, respectively. The national population-weighted CO concentrations were predicted to be (0.99 ± 0.30) mg m−3 (µ±σ) and showed decreasing trends over all regions of China at a rate of (−0.021 ± 0.004) mg m−3 per year. The CO pollution was more severe in North China (1.19 ± 0.30) mg m−3, and the predicted spatial pattern was roughly consistent with the MOPITT-CO. The hotspots in the Central Tibetan Plateau which were overlooked by the MOPITT were revealed by the refined RF-STK predictions. This information has an implication for improving the MOPITT-CO derivation procedure and air quality management.
Dongren Liu; Baofeng Di; Yuzhou Luo; Xunfei Deng; Hanyue Zhang; Fumo Yang; Michael L. Grieneisen; Yu Zhan. Estimating ground-level CO concentrations across China based on national monitoring network and MOPITT: Potentially overlooked CO hotspots in the Tibetan Plateau. 2019, 2019, 1 -26.
AMA StyleDongren Liu, Baofeng Di, Yuzhou Luo, Xunfei Deng, Hanyue Zhang, Fumo Yang, Michael L. Grieneisen, Yu Zhan. Estimating ground-level CO concentrations across China based on national monitoring network and MOPITT: Potentially overlooked CO hotspots in the Tibetan Plateau. . 2019; 2019 ():1-26.
Chicago/Turabian StyleDongren Liu; Baofeng Di; Yuzhou Luo; Xunfei Deng; Hanyue Zhang; Fumo Yang; Michael L. Grieneisen; Yu Zhan. 2019. "Estimating ground-level CO concentrations across China based on national monitoring network and MOPITT: Potentially overlooked CO hotspots in the Tibetan Plateau." 2019, no. : 1-26.