A database of atmospheric inorganic nitrogen deposition fluxes in China from satellite monitoring

Over the past century, atmospheric inorganic nitrogen (IN) deposition to terrestrial ecosystems has significantly increased and caused various environmental issues. China has been one of the hotspot regions for IN deposition, yet limited data exist regarding IN deposition fluxes in China at the regional scale. In this study, based on NO2 and NH3 columns acquired by satellite sensors, coupled with atmospheric chemical transport model (CTM), mixed-effects model and site observations, we constructed regional-scale IN dry and wet deposition models respectively, and finally proposed a spatially explicit database of IN deposition fluxes in China. The database includes the dry, wet and total deposition fluxes in China during 2011-2020, and the data are presented in raster form with a resolution of 0.25° × 0.25°. Overall, the database is of great importance for monitoring and simulating the trends of IN deposition over a long time series in China.

Unified theoretical framework for black carbon mixing state allows greater accuracy of climate effect estimation

Black carbon (BC) plays an important role in the climate system because of its strong warming effect, yet the magnitude of this effect is highly uncertain owing to the complex mixing state of aerosols. Here we build a unified theoretical framework to describe BC's mixing states, linking dynamic processes to BC coating thickness distribution, and show its self-similarity for sites in diverse environments. The size distribution of BC-containing particles is found to follow a universal law and is independent of BC core size. A new mixing state module is established based on this finding and successfully applied in global and regional models, which increases the accuracy of aerosol climate effect estimations. Our theoretical framework links observations with model simulations in both mixing state description and light absorption quantification.

Analysis and Modeling of Air Pollution in Extreme Meteorological Conditions: A Case Study of Jeddah, the Kingdom of Saudi Arabia

Air pollution has serious environmental and human health-related consequences; however, little work seems to be undertaken to address the harms in Middle Eastern countries, including Saudi Arabia. We installed a continuous air quality monitoring station in Jeddah, Saudi Arabia and monitored several air pollutants and meteorological parameters over a 2-year period (2018–2019). Here, we developed two supervised machine learning models, known as quantile regression models, to analyze the whole distribution of the modeled pollutants, not only the mean values. Two pollutants, namely NO₂ and O₃, were modeled by dividing their concentrations into several quantiles (0.05, 0.25, 0.50, 0.75, and 0.95) and the effect of several pollutants and meteorological variables was analyzed on each quantile. The effect of the explanatory variables changed at different segments of the distribution of NO₂ and O₃ concentrations. For instance, for the modeling of O₃, the coefficients of wind speed at quantiles 0.05, 0.25, 0.5, 0.75, and 0.95 were 1.40, 2.15, 2.34, 2.31, and 1.56, respectively. Correlation coefficients of 0.91 and 0.92 and RMSE values of 14.41 and 8.96, which are calculated for the cross-validated models of NO₂ and O₃, showed an acceptable model performance. Quantile analysis aids in better understanding the behavior of air pollution and how it interacts with the influencing factors.

Seasonal Dependence of Aerosol Data Assimilation and Forecasting Using Satellite and Ground-Based Observations

This study examines the performance of a data assimilation and forecasting system that simultaneously assimilates satellite aerosol optical depth (AOD) and ground-based PM10 and PM2.5 observations into the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). The data assimilation case for the surface PM10 and PM2.5 concentrations exhibits a higher consistency with the observed data by showing more correlation coefficients than the no-assimilation case. The data assimilation also shows beneficial impacts on the PM10 and PM2.5 forecasts for South Korea for up to 24 h from the updated initial condition. This study also finds deficiencies in data assimilation and forecasts, as the model shows a pronounced seasonal dependence of forecasting accuracy, on which the seasonal changes in regional atmospheric circulation patterns have a significant impact. In spring, the forecast accuracy decreases due to large uncertainties in natural dust transport from the continent by north-westerlies, while the model performs reasonably well in terms of anthropogenic emission and transport in winter. When the south-westerlies prevail in summer, the forecast accuracy increases with the overall reduction in ambient concentration. The forecasts also show significant accuracy degradation as the lead time increases because of systematic model biases. A simple statistical correction that adjusts the mean and variance of the forecast outputs to resemble those in the observed distribution can maintain the forecast skill at a practically useful level for lead times of more than a day. For a categorical forecast, the skill score of the data assimilation run increased by up to 37% compared to that of the case with no assimilation, and the skill score was further improved by 10% through bias correction.

Spatially resolved environmental fate models: A review

Spatially resolved environmental models are important tools to introduce and highlight the spatial variability of the real world into modeling. Although various spatial models have been developed so far, yet the development and evaluation of these models remain a challenging task due to several difficulties related to model setup, computational cost, and obtaining high-resolution input data (e.g., monitoring and emission data). For example, atmospheric transport models can be used when high resolution predicted concentrations in atmospheric compartments are required, while spatial multimedia fate models may be preferred for regulatory risk assessment, life cycle impact assessment of chemicals, or when the partitioning of chemical substances in a multimedia environment is considered. The goal of this paper is to review and compare different spatially resolved environmental models, according to their spatial, temporal and chemical domains, with a closer insight into spatial multimedia fate models, to achieve a better understanding of their strengths and limitations. This review also points out several requirements for further improvement of existing models as well as for their integration.

Spatiotemporal variation in near-surface CH4 concentrations in China over the last two decades

Methane is one of the main greenhouse trace gases and seriously affects the radiation balance of Earth systems due to its strong heat absorption capacity and long atmospheric retention time. Based on the methane stratification data simulated by the community atmospheric model with chemistry (CAM-chem), near-surface methane concentrations were estimated by utilizing the Gaussian function, and the spatiotemporal variation in the near-surface methane concentration in China from 2001 to 2019 was discussed in this research. The results show that (1) based on the methane stratification concentration data simulated by the atmospheric chemical model, the near-surface CH₄ concentration estimated by Gaussian function model is reliable, which provides a new method to estimate the near-surface CH₄ concentration over China; (2) from 2001 to 2019, the near-surface methane concentration in China showed an increasing trend with an annual growth rate of 7.20±0.23 ppb·a^-1. The annual maximum near-surface methane concentration was measured in winter, and the minimum was measured in summer; (3) the spatial distribution differences are obvious: the methane concentration in the east was higher than that in the west, and the methane concentration in the north was higher than that in the south. Moreover, the distributions of methane in the east and west are consistent with the division of Hu Huanyong population line.

Application of Density Plots and Time Series Modelling to the Analysis of Nitrogen Dioxides Measured by Low-Cost and Reference Sensors in Urban Areas

Temporal variability of NO2 concentrations measured by 28 Envirowatch E-MOTEs, 13 AQMesh pods, and eight reference sensors (five run by Sheffield City Council and three run by the Department for Environment, Food and Rural Affairs (DEFRA)) was analysed at different time scales (e.g., annual, weekly and diurnal cycles). Density plots and time variation plots were used to compare the distributions and temporal variability of NO2 concentrations. Long-term trends, both adjusted and non-adjusted, showed significant reductions in NO2 concentrations. At the Tinsley site, the non-adjusted trend was −0.94 (−1.12, −0.78) µgm−3/year, whereas the adjusted trend was −0.95 (−1.04, −0.86) µgm−3/year. At Devonshire Green, the non-adjusted trend was −1.21 (−1.91, −0.41) µgm−3/year and the adjusted trend was −1.26 (−1.57, −0.83) µgm−3/year. Furthermore, NO2 concentrations were analysed employing univariate linear and nonlinear time series models and their performance was compared with a more advanced time series model using two exogenous variables (NO and O3). For this purpose, time series data of NO, O3 and NO2 were obtained from a reference site in Sheffield, which were more accurate than the measurements from low-cost sensors and, therefore, more suitable for training and testing the model. In this article, the three main steps used for model development are discussed: (i) model specification for choosing appropriate values for p, d and q, (ii) model fitting (parameters estimation), and (iii) model diagnostic (testing the goodness of fit). The linear auto-regressive integrated moving average (ARIMA) performed better than the nonlinear counterpart; however, its performance in predicting NO2 concentration was inferior to ARIMA with exogenous variables (ARIMAX). Using cross-validation ARIMAX demonstrated strong association with the measured concentrations, with a correlation coefficient of 0.84 and RMSE of 9.90. ARIMAX can be used as an early warning tool for predicting potential pollution episodes in order to be proactive in adopting precautionary measures.

Analysis of Air Pollution in Urban Areas with Airviro Dispersion Model—A Case Study in the City of Sheffield, United Kingdom

Two air pollutants, oxides of nitrogen (NOx) and particulate matter (PM10), are monitored and modelled employing Airviro air quality dispersion modelling system in Sheffield, United Kingdom. The aim is to determine the most significant emission sources and their spatial variability. NOx emissions (ton/year) from road traffic, point and area sources for the year 2017 were 5370, 6774, and 2425, whereas those of PM10 (ton/year) were 345, 1449, and 281, respectively, which are part of the emission database. The results showed three hotspots of NOx, namely the Sheffield City Centre, Darnall and Tinsley Roundabout (M1 J34S). High PM10 concentrations were shown mainly between Sheffield Forgemasters International (a heavy engineering steel company) and Meadowhall Shopping Centre. Several emission scenarios were tested, which showed that NOx concentrations were mainly controlled by road traffic, whereas PM10 concentrations were controlled by point sources. Spatiotemporal variability and public exposure to air pollution were analysed. NOx concentration was greater than 52 µg/m3 in about 8 km2 area, where more than 66 thousand people lived. Models validated by observations can be used to fill in spatiotemporal gaps in measured data. The approach used presents spatiotemporal situation awareness maps that could be used for decision making and improving the urban infrastructure.

The fuel of atmospheric chemistry: Toward a complete description of reactive organic carbon

The Earth's atmosphere contains a multitude of emitted (primary) and chemically formed (secondary) gases and particles that degrade air quality and modulate the climate. Reactive organic carbon (ROC) species are the fuel of the chemistry of the atmosphere, dominating short-lived emissions, reactivity, and the secondary production of key species such as ozone, particulate matter, and carbon dioxide. Despite the central importance of ROC, the diversity and complexity of this class of species has been a longstanding obstacle to developing a comprehensive understanding of how the composition of our atmosphere, and the associated environmental implications, will evolve. Here, we characterize the role of ROC in atmospheric chemistry and the challenges inherent in measuring and modeling ROC, and highlight recent progress toward achieving mass closure for the complete description of atmospheric ROC.

Coupled Stratospheric Chemistry–Meteorology Data Assimilation. Part I: Physical Background and Coupled Modeling Aspects

A coupled stratospheric chemistry–meteorology model was developed by combining the Canadian operational weather prediction model Global Environmental Multiscale (GEM) with a comprehensive stratospheric photochemistry model from the Belgian Assimilation System for Chemical ObsErvations (BASCOE). The coupled model was called GEM-BACH for GEM-Belgian Atmospheric CHemistry. The coupling was made across a chemical interface that preserves time-splitting while being modular, allowing GEM to run with or without chemistry. An evaluation of the coupling was performed by comparing the coupled model, refreshed by meteorological analyses every 6 h, against the standard offline chemical transport model (CTM) approach. Results show that the dynamical meteorological consistency between meteorological analysis times far outweighs the error created by the jump resulting from the meteorological analysis increments at regular time intervals, irrespective of whether a 3D-Var or 4D-Var meteorological analysis is used. Arguments in favor of using the same horizontal resolution for chemistry, meteorology, and meteorological and chemical analysis increments are also presented. GEM-BACH forecasts refreshed by meteorological analyses every 6 h were compared against independent measurements of temperature, long-lived species, ozone and water vapor. The comparison showed a relatively good agreement throughout the stratosphere except for an upper-level warm temperature bias and an ozone deficit of nearly 15%. In particular, the coupled model simulation during an ozone hole event gives better ozone concentrations than a 4D-Var chemical assimilation at a lower resolution.

Improved Inversion of Monthly Ammonia Emissions in China Based on the Chinese Ammonia Monitoring Network and Ensemble Kalman Filter

Ammonia (NH₃) emission inventories are an essential input in chemical transport models and are helpful for policy-makers to refine mitigation strategies. However, current estimates of Chinese NH₃ emissions still have large uncertainties. In this study, an improved inversion estimation of NH₃ emissions in China has been made using an ensemble Kalman filter and the Nested Air Quality Prediction Modeling System. By first assimilating the surface NH₃ observations from the Ammonia Monitoring Network in China at a high resolution of 15 km, our inversion results have provided new insights into the spatial and temporal patterns of Chinese NH₃ emissions. More enhanced NH₃ emission hotspots, likely associated with industrial or agricultural sources, were captured in northwest China, where the a posteriori NH₃ emissions were more than twice the a priori emissions. Monthly variations of NH₃ emissions were optimized in different regions of China and exhibited a more distinct seasonality, with the emissions in summer being twice those in winter. The inversion results were well-validated by several independent datasets that traced gaseous NH₃ and related atmospheric processes. These findings highlighted that the improved inversion estimation can be used to advance our understanding of NH₃ emissions in China and their environmental impacts.

Assessment of ozone toxicity among 14 Indian wheat cultivars under field conditions: growth and productivity

Tropospheric ozone (O₃) is a well-known threat to global agricultural production. Wheat (Triticum aestivum L.) is the second most important staple crop in India, although little is known about intra-specific variability of Indian wheat cultivars in terms of their sensitivity against O₃. In this study, 14 wheat cultivars widely grown in India were exposed to 30 ppb elevated O₃ above ambient level using open top chambers to evaluate their response against O₃ stress. Different growth and physiological parameters, foliar injury and grain yield were evaluated to assess the sensitivity of cultivars and classified them on the basis of their cumulative stress response index (CSRI). Due to elevated O₃, growth parameters, plant biomass, and photosynthetic rates were negatively affected, whereas variable reductions in yield were observed among the test cultivars. Based on CSRI values, HD 2987, DBW 50, DBW 77, and PBW 550 were classified as O₃ sensitive; HD 2967, NIAW 34, HD 3059, PBW 502, HUW 213, and HUW 251 as intermediately sensitive, while HUW12, KUNDAN, HUW 55, and KHARCHIYA 65 were found to be O₃-tolerant cultivars. Cultivars released after year 2000 were found to be more sensitive compared to earlier released cultivars. Path analysis approach showed that leaf area, plant biomass, stomatal conductance, net assimilation rate, and absolute growth rate were the most important variables influencing yield under O₃ stress. Findings of the current study highlight the importance of assessing differential sensitivity and tolerance of wheat cultivars and response of different traits in developing resistance against elevated O₃.

Spatio-temporal assessment and seasonal variation of tropospheric ozone in Pakistan during the last decade

This study uses the tropospheric ozone data derived from combined observations of Ozone Monitoring Instrument/Microwave Limb Sounder instruments by using the tropospheric ozone residual method. The main objective was to study the spatial distribution and temporal evolution in the troposphere ozone columns over Pakistan during the time period of 2004 to 2014. Results showed an overall increase of 3.2 ± 1.1 DU in tropospheric ozone columns over Pakistan. Spatial distribution showed enhanced ozone columns in the Punjab and southern Sindh consistent to high population, urbanization, and extensive anthropogenic activities, and exhibited statistically significant temporal increase. Seasonal variations in tropospheric ozone columns are driven by various factors such as seasonality in UV-B fluxes, seasonality in ozone precursor gases such as NO_x and volatile organic compounds (caused by temperature dependent biogenic emission) and agricultural fire activities in Pakistan. A strong correlation of 96% (r = 0.96) was found between fire events and tropospheric ozone columns in Pakistan.

Tropospheric ozone pollution in India: effects on crop yield and product quality

Ozone (O₃) in troposphere is the most critical secondary air pollutant, and being phytotoxic causes substantial losses to agricultural productivity. Its increasing concentration in India particularly in Indo-Gangetic plains is an issue of major concern as it is posing a threat to agriculture. In view of the issue of rising surface level of O₃ in India, the aim of this compilation is to present the past and the prevailing concentrations of O₃ and its important precursor (oxides of nitrogen) over the Indian region. The resulting magnitude of reductions in crop productivity as well as alteration in the quality of the product attributable to tropospheric O₃ has also been taken up. Studies in relation to yield measurements have been conducted predominantly in open top chambers (OTCs) and also assessed by using antiozonant ethylene diurea (EDU). There is a substantial spatial difference in O₃ distribution at different places displaying variable O₃ concentrations due to seasonal and geographical variations. This review further recognizes the major information lacuna and also highlights future perspectives to get the grips with rising trend of ground level O₃ pollution and also to formulate the policies to check the emissions of O₃ precursors in India.

A hybrid model for spatially and temporally resolved ozone exposures in the continental United States

Ground-level ozone is an important atmospheric oxidant, which exhibits considerable spatial and temporal variability in its concentration level. Existing modeling approaches for ground-level ozone include chemical transport models, land-use regression, Kriging, and data fusion of chemical transport models with monitoring data. Each of these methods has both strengths and weaknesses. Combining those complementary approaches could improve model performance. Meanwhile, satellite-based total column ozone, combined with ozone vertical profile, is another potential input. The authors propose a hybrid model that integrates the above variables to achieve spatially and temporally resolved exposure assessments for ground-level ozone. The authors used a neural network for its capacity to model interactions and nonlinearity. Convolutional layers, which use convolution kernels to aggregate nearby information, were added to the neural network to account for spatial and temporal autocorrelation. The authors trained the model with the Air Quality System (AQS) 8-hr daily maximum ozone in the continental United States from 2000 to 2012 and tested it with left out monitoring sites. Cross-validated R² on the left out monitoring sites ranged from 0.74 to 0.80 (mean 0.76) for predictions on 1 km × 1 km grid cells, which indicates good model performance. Model performance remains good even at low ozone concentrations. The prediction results facilitate epidemiological studies to assess the health effect of ozone in the long term and the short term.

IMPLICATIONS

Ozone monitors do not provide full data coverage over the United States, which is an obstacle to assess the health effect of ozone when monitoring data are not available. This paper used a hybrid approach to combine satellite-based ozone measurements, chemical transport model simulations, land-use terms, and other auxiliary variables to obtain spatially and temporally resolved ground-level ozone estimation.

Relationship of polycyclic aromatic hydrocarbons with oxy(quinone) and nitro derivatives during air mass transport

Airborne concentrations of Polycyclic Aromatic Hydrocarbons (PAH), quinone and nitro derivatives have been measured at three sites on the coast of Saudi Arabia to the north of the city of Jeddah. The PAH show a general reduction in concentrations from northwest to southeast, consistent with a source from a petrochemical works to the northwest of the sampling sites. In comparison, the concentrations of quinones show little variation between the sampling sites consistent with these being predominantly longer lived secondary pollutants formed from PAH oxidation. The nitro-PAH show a gradient in concentrations similar to but smaller than that for the PAH suggesting a balance between atmospheric formation and removal by photolysis. The 2-nitrofluoranthene:1-nitropyrene ratio increases from north to south, consistent with atmospheric chemical formation of the former compound, while the ratio of 2-nitrofluoranthene:2-nitropyrene is consistent with hydroxyl radical as the dominant reactant. An investigation of the changes in PAH congener ratios during air mass transport along the Red Sea coast shows consistency with reaction with a relatively low concentration of hydroxyl radical only for the day with the highest concentrations. It is concluded that while PAH degradation is occurring by chemical reaction, emissions from other locations along the air mass trajectory are most probably also leading to changes in congener ratios.

Uncertainties influencing health-based prioritization of ozone abatement options

The primary goal of air quality management is protection of human health. Therefore, formulation of ground-level ozone mitigation policies could be informed by considering not just attainment of regulatory standards but also how control measures benefit public health. However, evaluation of health impacts is complicated by uncertainties associated with photochemical modeling and epidemiological studies. This study demonstrates methods to characterize uncertainties influencing health-benefits estimation of ozone reduction (averted premature mortalities due to short-term exposure) in the Dallas-Fort Worth (DFW) region. Uncertainty in photochemical modeling and the selection of temporal metric (duration of ozone exposure) for concentration-response relationships can each affect the health-based prioritization of ozone control options. For example, deterministic results (neglecting uncertainties) based on 8-h daily maximum ozone reduction shows DFW anthropogenic NO(x) controls to yield 9.23 times as much benefit per ton as VOC controls. However, the rankings reverse under 5.7% of the cases (including 2.8% cases that exhibit incremental mortalities due to NO(X) control) when uncertainties in the photochemical model are considered. Evaluated ozone exposure on a 24-h rather than an 8-h basis also reverses the rankings.

Massive global ozone loss predicted following regional nuclear conflict

We use a chemistry-climate model and new estimates of smoke produced by fires in contemporary cities to calculate the impact on stratospheric ozone of a regional nuclear war between developing nuclear states involving 100 Hiroshima-size bombs exploded in cities in the northern subtropics. We find column ozone losses in excess of 20% globally, 25-45% at midlatitudes, and 50-70% at northern high latitudes persisting for 5 years, with substantial losses continuing for 5 additional years. Column ozone amounts remain near or <220 Dobson units at all latitudes even after three years, constituting an extratropical "ozone hole." The resulting increases in UV radiation could impact the biota significantly, including serious consequences for human health. The primary cause for the dramatic and persistent ozone depletion is heating of the stratosphere by smoke, which strongly absorbs solar radiation. The smoke-laden air rises to the upper stratosphere, where removal mechanisms are slow, so that much of the stratosphere is ultimately heated by the localized smoke injections. Higher stratospheric temperatures accelerate catalytic reaction cycles, particularly those of odd-nitrogen, which destroy ozone. In addition, the strong convection created by rising smoke plumes alters the stratospheric circulation, redistributing ozone and the sources of ozone-depleting gases, including N(2)O and chlorofluorocarbons. The ozone losses predicted here are significantly greater than previous "nuclear winter/UV spring" calculations, which did not adequately represent stratospheric plume rise. Our results point to previously unrecognized mechanisms for stratospheric ozone depletion.

Diurnal variations in H2O2, O3, PAN, HNO3 and aldehyde concentrations and NO/NO2 ratios at Rishiri Island, Japan: potential influence from iodine chemistry

The presence of iodine chemistry, hypothesized due to the overprediction of HO(2) levels by a photochemical box model at Rishiri Island in June 2000, was quantitatively tested against the observed NO/NO(2) ratios and the net production rates of ozone. The observed NO/NO(2) ratios were reproduced reasonably well by considering the conversion of NO to NO(2) by IO, whose amount was calculated so as to reproduce the observed HO(2) levels. However, the net production rates of ozone were calculated to be negative when such high mixing ratios of IO were considered, which was inconsistent with the observed buildup of ozone during daytime. These results suggest that iodine chemistry may not be the sole mechanism for the reduced mixing ratios of HO(2), or that "hot spots" for iodine chemistry were present. Diurnal variations in the mixing ratios of HCHO, CH(3)CHO, peroxy acetyl nitrate (PAN) and HNO(3) observed during the study are presented along with the simulated ones. The box model simulations suggest that the effect of iodine chemistry on these concentrations is small and that important sources of CH(3)CHO and sinks of PAN are probably missing from our current understanding of the tropospheric chemistry mechanism.