A novel approach to deriving the fine-scale daily NO2 dataset during 2005-2020 in China: Improving spatial resolution and temporal coverage to advance exposure assessment.

Surface NO2 pollution can result in serious health consequences such as cardiovascular disease, asthma, and premature mortality. Due to the extensive spatial variation in surface NO2, the spatial resolution of a NO2 dataset has a significant impact on the exposure and health impact assessment. There is currently no long-term, high-resolution, and publicly available NO2 dataset for China. To fill this gap, this study generated a NO2 dataset named RBE-DS-NO2 for China during 2005-2020 at 1 km and daily resolution. We employed the robust back-extrapolation via a data augmentation approach (RBE-DA) to ensure the predictive accuracy in back-extrapolation before 2013, and utilized an improved spatial downscaling technique (DS) to refine the spatial resolution from 10 km to 1 km. Back-extrapolation validation based on 2005-2012 observations from sites in Taiwan province yielded an R2 of 0.72 and RMSE of 10.7 µg/m3, while cross-validation across China during 2013-2020 showed an R2 of 0.73 and RMSE of 9.6 µg/m3. RBE-DS-NO2 better captured spatiotemporal variation of surface NO2 in China compared to the existing publicly available datasets. Exposure assessment using RBE-DS-NO2 show that the population living in non-attainment areas (NO2 ≥ 30 µg/m3) grew from 376 million in 2005 to 612 million in 2012, then declined to 404 million by 2020. Unlike this national trend, exposure levels in several major cities (e.g., Shanghai and Chengdu) continued to increase during 2012-2020, driven by population growth and urban migration. Furthermore, this study revealed that low-resolution dataset (i.e., the 10 km intermediate dataset before the downscaling) overestimated NO2 levels, due to the limited specificity of the low-resolution model in simulating the relationship between NO2 and the predictor variables. Such limited specificity likely biased previous long-term NO2 exposure and health impact studies employing low-resolution datasets. The RBE-DS-NO2 dataset enables robust long-term assessments of NO2 exposure and health impacts in China.

Assessment of agricultural pesticide inert ingredient transport following modeling approach: Case study of two formulation agents in Sacramento River watershed.

This research provides the first assessment of the environmental fate and transport of agricultural pesticide formulation agents following a dynamic modeling approach. Two formulation agents of toxicological concern, Naphthalene and Solvent Naphtha (Petroleum), Heavy Aromatic, were simulated from their usage in commercially-applied pesticides. The Soil and Water Assessment Tool (SWAT) was applied to simulate these formulation agents during 2011-2014 in the agriculturally intensive Sacramento River watershed. The sensitivity and uncertainty of some key parameters were analyzed. The predicted transport masses of these formulation agents in surface water were strongly associated with rainfall. While predicted transport masses were quite small at the watershed scale (<0.01% of applied masses), they were 26-31 times higher in certain locales at the subbasin level. Since many formulation agents are widely used in pesticides throughout this and other agriculturally impacted watersheds, their potential risks in the environment need more thorough investigation by modeling and monitoring, especially for areas with heavy usage.

A review of datasets and methods for deriving spatiotemporal distributions of atmospheric CO2.

As the most abundant greenhouse gas, atmospheric carbon dioxide (CO2) is considered one of the main attributors to climate change. Atmospheric CO2 concentrations can be measured by ground-based monitoring networks, mobile monitoring campaigns, and carbon-observing satellites. However, the worldwide ground-based monitoring networks are composed of sparsely distributed sites and are inadequate to represent the spatiotemporal distributions of CO2. Satellite-based remote sensing features repeated, long-term, and large-scale measurements, so it plays a crucial role in monitoring the global distributions of atmospheric CO2. However, due to the presence of heavy clouds (or aerosols) and the limitation of satellite orbiting tracks, there exist large amounts of missing data in satellite retrievals. Various methods, including chemical transport models (CTMs), geostatistical methods, and regression-based models, have been employed to derive full-coverage spatiotemporal distributions of CO2 based on the limited CO2 measurements. This review summarizes the strengths and limitations of these methods. However, CTMs simulation results can have high uncertainty due to imperfect knowledge of the real world, and the interpolation accuracy of all geostatistical methods is limited by the large amount of data gaps in current satellite retrieved CO2 products. To overcome these limitations, regression-based methods (especially machine learning models) have the ability to predict CO2 with superior predictive performance, so this review also summarizes the framework of the machine learning approach. Leveraging the ongoing advancements of satellite instrumentation, the satellite-based CO2 products have been improving dramatically in recent decades, and this review will describe and critically assess the advantages and disadvantages of the currently used systems in detail. For future improvements, we recommend the fusion of data from multiple satellite retrievals and CTMs by using machine learning algorithms in order to obtain even longer-term, larger-scale, finer-resolution, and higher-accuracy CO2 datasets.

A data-augmentation approach to deriving long-term surface SO2 across Northern China: Implications for interpretable machine learning.

Until recently, Northern China was one of the most SO2 polluted regions in the world. The lack of long-term and spatially resolved surface SO2 data hinders retrospective evaluation of relevant environmental policies and human health effects. This study aims to derive the spatiotemporal distribution of surface SO2 across Northern China during 2005-2019. As "concept drift" causes substantial estimation bias in back-extrapolation, we propose a new approach named the robust back-extrapolation via data augmentation approach (RBE-DA) to model the long-term surface SO2. The results show that the population-weighted regional SO2 ([SO2]pw) increased from 2005 to 2007 and decreased steadily afterwards. The [SO2]pw decreased by 80.4% from 74.2 ± 28.4 µg/m3 in 2007 to 14.6 ± 4.8 µg/m3 in 2019. The predicted spatial distributions for each year show that the SO2 pollution was severe (more than 20 µg/m3) in most areas of Northern China until 2017. By using model interpretation methods, we visually reveal the mechanism of estimation bias in the back-extrapolation. Specifically, the training data is severely imbalanced with respect to the satellite-retrieved SO2 column densities (i.e., it is short on high-value samples), so the benchmark model is unable to extrapolate the effects of this important predictor. This study provides long-term surface SO2 data for post hoc evaluation and human exposure assessment in Northern China, while demonstrating that the interpretable machine learning approach is critical for model diagnostics and refinement. Leveraging satellite retrievals, the RBE-DA approach can be applied worldwide to back-extrapolate various measures of air quality.

A robust approach to deriving long-term daily surface NO2 levels across China: Correction to substantial estimation bias in back-extrapolation.

BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Assessment of pyrethroid contamination and potential mitigation strategies in California Central Coast surface waters.

Increased use of pyrethroids in the Central Coast of California since 2011 has resulted in a dramatic increase in the number and proportion of surface water samples with detectable concentrations at levels of concern to the public and state regulators. The goals of this study were to investigate the relationships between pyrethroid usage and environmental contamination, quantify and assess the potential risks, and recommend mitigation strategies. This study compiled the available pyrethroid use and surface water sampling data for the region, and then applied GIS methods to dynamic simulation modeling and usage-restriction buffer analyses. The results showed that in Monterey County alone, the agricultural usages of bifenthrin and permethrin each increased by ~50%, and the positive detection frequencies of both also increased around 2011-2013. County-wide, bifenthrin positive detections in surface water samples increased precipitously from 8.2% (7/85) for 2008-2012 up to 36.4% (106/291) for 2013-2017, and detections above its crustacean LC50 concentration went from 7.1% (6/85) to 35.7% (104/291). Despite its higher usage by mass, comparable figures for permethrin were more modest for the same time-periods, with positive detections going from 10.6% (9/85) to 14.4% (64/444), and detections above its crustacean LC50 going from 3.5% (3/85) to 7.2% (32/444). The seasonal lag between high bifenthrin usage in spring/summer and high detections in fall/winter samples showed the best correlations with 128- to 182-day lag times. This timing suggests that fallow season rain is likely the main driver of pyrethroid off-site movement into surface waters. SWAT modeling indicated that significant reductions in surface water permethrin concentrations only occurred with buffer distances of 1.6-3.2 km, but not with narrower buffers. However, if those wider buffers were implemented, permethrin could no longer be used on the majority of land where it is currently applied. Specifically, a 1.6-km buffer reduced the instream concentration by 8% but impacted 50% of the cropland, and a 3.2-km buffer reduced the concentration by 50% while impacting 76% of cropland. This study suggested that more promising alternative management practices could include an overall reduction in pyrethroid usage back to 2011 levels or other active mitigation strategies, like planting cover crops during the fallow winter wet season, or installing either vegetated buffer strips and/or sediment check dams on small tributaries to minimize sediment runoff.

A nonparametric approach to filling gaps in satellite-retrieved aerosol optical depth for estimating ambient PM2.5 levels.

Satellite-retrieved aerosol optical depth (AOD) is commonly used to estimate ambient levels of fine particulate matter (PM2.5), though it is important to mitigate the estimation bias of PM2.5 due to gaps in satellite-retrieved AOD. A nonparametric approach with two random-forest submodels is proposed to estimate PM2.5 levels by filling gaps in satellite-retrieved AOD. This novel approach was employed to estimate the spatiotemporal distribution of daily PM2.5 levels during 2013-2015 in the Sichuan Basin of Southwest China, where the coverage rate of composite AOD retrieved by the Terra and Aqua satellites was only 11.7%. Based on the retrieved AOD and various covariates (including meteorological conditions and land use types), the first random-forest submodel (named AOD-submodel) was trained to fill the gaps in the AOD dataset, giving a cross-validation R2 of 0.95. Subsequently, the second random-forest submodel (named PM2.5-submodel) was trained to estimate the PM2.5 levels for unmonitored areas/days based on the gap-filled AOD, ground-monitored PM2.5 levels, and the covariates, and achieved a cross-validation R2 of 0.86. By comparing the complete and incomplete (i.e., without the days when AOD data were missing) estimates, we found that the monthly PM2.5 levels could be overestimated by 34.6% if the PM2.5 values coincident with AOD gaps were not considered. The newly developed approach is valuable for deriving the complete spatiotemporal distribution of daily PM2.5 from incomplete remote-sensing data, which is essential for air quality management and human exposure assessment.

Satellite-Based Estimates of Daily NO2 Exposure in China Using Hybrid Random Forest and Spatiotemporal Kriging Model.

A novel model named random-forest-spatiotemporal-kriging (RF-STK) was developed to estimate the daily ambient NO2 concentrations across China during 2013-2016 based on the satellite retrievals and geographic covariates. The RF-STK model showed good prediction performance, with cross-validation R2 = 0.62 (RMSE = 13.3 µg/m3) for daily and R2 = 0.73 (RMSE = 6.5 µg/m3) for spatial predictions. The nationwide population-weighted multiyear average of NO2 was predicted to be 30.9 ± 11.7 µg/m3 (mean ± standard deviation), with a slowly but significantly decreasing trend at a rate of -0.88 ± 0.38 µg/m3/year. Among the main economic zones of China, the Pearl River Delta showed the fastest decreasing rate of -1.37 µg/m3/year, while the Beijing-Tianjin Metro did not show a temporal trend ( P = 0.32). The population-weighted NO2 was predicted to be the highest in North China (40.3 ± 10.3 µg/m3) and lowest in Southwest China (24.9 ± 9.4 µg/m3). Approximately 25% of the population lived in nonattainment areas with annual-average NO2 > 40 µg/m3. A piecewise linear function with an abrupt point around 100 people/km2 characterized the relationship between the population density and the NO2, indicating a threshold of aggravated NO2 pollution due to urbanization. Leveraging the ground-level NO2 observations, this study fills the gap of statistically modeling nationwide NO2 in China, and provides essential data for epidemiological research and air quality management.

Spatiotemporal prediction of daily ambient ozone levels across China using random forest for human exposure assessment.

In China, ozone pollution shows an increasing trend and becomes the primary air pollutant in warm seasons. Leveraging the air quality monitoring network, a random forest model is developed to predict the daily maximum 8-h average ozone concentrations ([O3]MDA8) across China in 2015 for human exposure assessment. This model captures the observed spatiotemporal variations of [O3]MDA8 by using the data of meteorology, elevation, and recent-year emission inventories (cross-validation R2 = 0.69 and RMSE = 26 µg/m3). Compared with chemical transport models that require a plenty of variables and expensive computation, the random forest model shows comparable or higher predictive performance based on only a handful of readily-available variables at much lower computational cost. The nationwide population-weighted [O3]MDA8 is predicted to be 84 ± 23 µg/m3 annually, with the highest seasonal mean in the summer (103 ± 8 µg/m3). The summer [O3]MDA8 is predicted to be the highest in North China (125 ± 17 µg/m3). Approximately 58% of the population lives in areas with more than 100 nonattainment days ([O3]MDA8>100 µg/m3), and 12% of the population are exposed to [O3]MDA8>160 µg/m3 (WHO Interim Target 1) for more than 30 days. As the most populous zones in China, the Beijing-Tianjin Metro, Yangtze River Delta, Pearl River Delta, and Sichuan Basin are predicted to be at 154, 141, 124, and 98 nonattainment days, respectively. Effective controls of O3 pollution are urgently needed for the highly-populated zones, especially the Beijing-Tianjin Metro with seasonal [O3]MDA8 of 140 ± 29 µg/m3 in summer. To the best of the authors' knowledge, this study is the first statistical modeling work of ambient O3 for China at the national level. This timely and extensively validated [O3]MDA8 dataset is valuable for refining epidemiological analyses on O3 pollution in China.

Aliphatic hydrocarbons recovered in vegetables from soils based on their in-situ distribution in various soil humus fractions using a successive extraction method.

Aliphatic hydrocarbons (AHs) are major petroleum contaminants in the environment. In this study, the AHs bound to various soil endogenetic humus fractions were separated through successive extraction. Most of the AHs (46.1%) in soils were adsorbed onto/into humic acids (HA) and a small quantity of AHs (9.6%) were organic solvent extractable. AHs in B. chinensis were also analyzed since their potential risks to the residents through ingestion. AHs from C21 to C34, so called high molecular weight AHs (HMWAHs), were dominant AHs in B. chinensis (85.5%) and soils (70.4%), followed by AHs from C16 to C21, whose mobility can be enhanced via binding to fulvic acids and then can be taken up by plant root lipids (soil-plant pathway). HMWAHs were mainly HA-bound and then were detained in the top soil layers. HMWAHs associated with fine topsoil particles could be transported to B. chinensis via the soil-air-plant pathway, including resuspension and aboveground plant cuticle capture. Results from Principal Component Analysis combined with Regression Analysis supported this assumption due to the positive correlations between HMWAHs concentration in B. chinensis and fine particle contents in soils. This work presents the distributions of petroleum contaminants that result from previously described behavior mechanisms.

Modeling pesticide diuron loading from the San Joaquin watershed into the Sacramento-San Joaquin Delta using SWAT.

Quantifying pesticide loading into the Sacramento-San Joaquin Delta of northern California is critical for water quality management in the region, and potentially useful for biological weed control planning. In this study, the Soil and Water Assessment Tool (SWAT) was applied to model streamflow, sediment, and pesticide diuron loading in the San Joaquin watershed, a major contributing area to the elevated pesticide levels in the downstream Delta. The Sequential Uncertainty Fitting version 2 (SUFI-2) algorithm was employed to perform calibration and uncertainty analysis. A combination of performance measures (PMs) and standardized performance evaluation criteria (PEC) was applied to evaluate model performance, while prediction uncertainty was quantified by 95% prediction uncertainty band (95PPU). Results showed that streamflow simulation was at least "satisfactory" at most stations, with more than 50% of the observed data bracketed by the 95PPU. Sediment simulation was rated as at least "satisfactory" based on two PMs, and diuron simulation was judged as "good" by all PMs. The 95PPU of sediment and diuron bracketed about 40% and 30% of the observed data, respectively. Significant correlations were observed between the diuron loads, and precipitation, streamflow, and the current and antecedent pesticide use. Results also showed that the majority (>70%) of agricultural diuron was transported during winter months, when direct exposure of biocontrol agents to diuron runoff is limited. However, exposure in the dry season could be a concern because diuron is relatively persistent in aquatic system. This study not only provides valuable information for the development of biological weed control plan in the Delta, but also serves as a foundation for the continued research on calibration, evaluation, and uncertainty analysis of spatially distributed, physically based hydrologic models.

Predicting pesticide removal efficacy of vegetated filter strips: A meta-regression analysis.

Vegetated Filter Strips (VFS's) are widely used for alleviating agricultural pesticide loadings to surface water bodies. However, effective tools are lacking to quantify the performance of VFS's in reducing off-site pesticide transport. In this study, we applied meta-regression to develop a model for predicting VFS pesticide retention efficiency based on hydrologic responses of VFS's, incoming pollutant characteristics and the interaction within and between these two factor groups (R(2)=0.83). In cross-validation analysis, our model (Q(2)=0.81) outperformed the existing pesticide retention module of VFSMOD (Q(2)=0.72) by explicitly accounting for interaction effect and the categorical effect of pesticide adsorption properties. Based on the 181 data points studied, infiltration had a leading, positive influence on pesticide retention, followed by sedimentation and interaction between the two. Interaction between infiltration and pesticide adsorption properties was also prominent, as the influence of infiltration was significantly lower for strongly adsorbed pesticides. In addition, the clay content of incoming sediment was negatively associated with pesticide retention. Our model is not only valuable in predicting VFS performance, but also provides a quantitative characterization of the interacting VFS processes, thereby facilitating a deeper understanding of the underlying mechanisms.

Botanical insecticide research: many publications, limited useful data.

Our analysis of >20000 papers on botanical insecticides from 1980 to 2012, indicates major growth in the number of papers published annually (61 in 1980 to 1207 in 2012), and their proportion among all papers on insecticides (1.43% in 1980 to 21.38% in 2012). However, only one-third of 197 random articles among the 1086 papers on botanical insecticides published in 2011 included any chemical data or characterization; and only a quarter of them included positive controls. Therefore, a substantial portion of recently published studies has design flaws that limit reproducibility and comparisons with other and/or future studies. In our opinion, much of the scientific literature on this subject is of limited use in the progress toward commercialization or advancement of knowledge, given the resources expended.

A comprehensive survey of retracted articles from the scholarly literature.

BACKGROUND: The number of retracted scholarly articles has risen precipitously in recent years. Past surveys of the retracted literature each limited their scope to articles in PubMed, though many retracted articles are not indexed in PubMed. To understand the scope and characteristics of retracted articles across the full spectrum of scholarly disciplines, we surveyed 42 of the largest bibliographic databases for major scholarly fields and publisher websites to identify retracted articles. This study examines various trends among them. RESULTS: We found, 4,449 scholarly publications retracted from 1928-2011. Unlike Math, Physics, Engineering and Social Sciences, the percentages of retractions in Medicine, Life Science and Chemistry exceeded their percentages among Web of Science (WoS) records. Retractions due to alleged publishing misconduct (47%) outnumbered those due to alleged research misconduct (20%) or questionable data/interpretations (42%). This total exceeds 100% since multiple justifications were listed in some retraction notices. Retraction/WoS record ratios vary among author affiliation countries. Though widespread, only miniscule percentages of publications for individual years, countries, journals, or disciplines have been retracted. Fifteen prolific individuals accounted for more than half of all retractions due to alleged research misconduct, and strongly influenced all retraction characteristics. The number of articles retracted per year increased by a factor of 19.06 from 2001 to 2010, though excluding repeat offenders and adjusting for growth of the published literature decreases it to a factor of 11.36. CONCLUSIONS: Retracted articles occur across the full spectrum of scholarly disciplines. Most retracted articles do not contain flawed data; and the authors of most retracted articles have not been accused of research misconduct. Despite recent increases, the proportion of published scholarly literature affected by retraction remains very small. Articles and editorials discussing retractions, or their relation to research integrity, should always consider individual cases in these broad contexts. However, better mechanisms are still needed for raising researchers' awareness of the retracted literature in their field.