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Traditional discharge monitoring usually relies on measuring flow velocity and cross-section area with various velocimeters or remote-sensing approaches. However, the topography of mountain streams in remote sites largely hinders the applicability of velocity–area methods. Here, we present a method to continuously monitor mountain stream discharge using a low-cost commercial camera and deep learning algorithm. A procedure of automated image categorization and discharge classification was developed to extract information on flow patterns and volumes from high-frequency red–green–blue (RGB) images with deep convolutional neural networks (CNNs). The method was tested at a small, steep, natural stream reach in southern China. Reference discharge data were acquired from a V-shaped weir and ultrasonic flowmeter installed a few meters downstream of the camera system. Results show that the discharge-relevant stream features implicitly embedded in RGB information can be effectively recognized and retrieved by CNN to achieve satisfactory performance in discharge measurement. Coupling between CNNs and traditional machine learning models (e.g., support vector machine and random forest) can potentially synthesize individual models' diverse merits and improve generalization performance. Besides, proper image pre-processing and categorization are critical for enhancing the robustness and applicability of the method under environmental disturbances (e.g., weather and vegetation on river banks). Our study highlights the usefulness of deep learning in analyzing complex flow images and tracking flow changes over time, which provides a reliable and flexible alternative apparatus for continuous discharge monitoring of rocky mountain streams. [ABSTRACT FROM AUTHOR]

We apply a local ensemble transform Kalman smoother (LETKS) in combination with the global aerosol–climate model ECHAM–HAM to estimate aerosol emissions from POLDER-3/PARASOL (POLarization and Directionality of the Earth's Reflectances) observations for the year 2006. We assimilate aerosol optical depth at 550 mnm (AOD550), the Ångström exponent at 550 and 865 nm (AE550–865), and single-scattering albedo at 550 nm (SSA550) in order to improve modeled aerosol mass, size and absorption simultaneously. The new global aerosol emissions increase to 1419 Tgyr-1 (+ 28 %) for dust, 1850 Tgyr-1 (+ 75 %) for sea salt, 215 Tgyr-1 (+ 143 %) for organic aerosol and 13.3 Tgyr-1 (+ 75 %) for black carbon, while the sulfur dioxide emissions increase to 198 Tgyr-1 (+ 42 %) and the total deposition of sulfates to 293 Tgyr-1 (+ 39 %). Organic and black carbon emissions are much higher than their prior values from bottom-up inventories, with a stronger increase in biomass burning sources (+ 193 % and + 90 %) than in anthropogenic sources (115 % and 70 %). The evaluation of the experiments with POLDER (assimilated) and AERONET as well as MODIS Dark Target (independent) observations shows a clear improvement compared with the ECHAM–HAM control run. Specifically based on AERONET, the global mean error in AOD550 improves from - 0.094 to - 0.006, while absorption aerosol optical depth at 550 nm (AAOD550) improves from - 0.009 to - 0.004 after the assimilation. A smaller improvement is also observed in the AE550–865 mean absolute error (from 0.428 to 0.393), with a considerably higher improvement over isolated island sites at the ocean. The new dust emissions are closer to the ensemble median of AEROCOM I, AEROCOM III and CMIP5 as well as some of the previous assimilation studies. The new sea salt emissions have become closer to the reported emissions from previous studies. Indications of a missing fraction of coarse dust and sea salt particles are discussed. The biomass burning changes (based on POLDER) can be used as alternative biomass burning scaling factors for the Global Fire Assimilation System (GFAS) inventory distinctively estimated for organic carbon (2.93) and black carbon (1.90) instead of the recommended scaling of 3.4 (Kaiser et al., 2012). The estimated emissions are highly sensitive to the relative humidity due to aerosol water uptake, especially in the case of sulfates. We found that ECHAM–HAM, like most of the global climate models (GCMs) that participated in AEROCOM and CMIP6, overestimated the relative humidity compared with ERA5 and as a result the water uptake by aerosols, assuming the kappa values are not underestimated. If we use the ERA5 relative humidity, sulfate emissions must be further increased, as modeled sulfate AOD is lowered. Specifically, over East Asia, the lower AOD can be attributed to the underestimated precipitation and the lack of simulated nitrates in the model. [ABSTRACT FROM AUTHOR]

Traditional discharge monitoring usually relies on measuring flow velocity and cross-section area with various velocimeters or remote-sensing approaches. However, the topography of mountain streams in remote sites largely hinders the applicability of velocity-area methods. We here present a method to continuously monitor mountain stream discharge using a low-cost commercial camera and deep learning algorithm. A procedure of automated image categorization and discharge classification was developed to extract information on flow patterns and volumes from high-frequency red–green–blue (RGB) images with deep convolutional neural networks (CNNs). The method was tested at a small, steep, natural stream reach in southern China. Reference discharge data was acquired from a V-shaped weir and ultrasonic flowmeter installed a few meters downstream of the camera system. Results show that the discharge-relevant stream features implicitly embedded in RGB information can be effectively recognized and retrieved by CNN to achieve satisfactory accuracy in discharge measurement. Coupling CNN and traditional machine learning models (e.g., support vector machine and random forest) can potentially synthesize individual models' diverse merits and improve generalization performance. Besides, proper image pre-processing and categorization are critical for enhancing the robustness and applicability of the method under environmental disturbances (e.g., weather and vegetation on river banks). Our study highlights the usefulness of deep learning in analyzing complex flow images and tracking flow changes over time, which provides a reliable and flexible alternative apparatus for continuous discharge monitoring of rocky mountain streams. [ABSTRACT FROM AUTHOR]

Global models are widely used to simulate biomass burning aerosol (BBA). Exhaustive evaluations on model representation of aerosol distributions and properties are fundamental to assess health and climate impacts of BBA. Here we conducted a comprehensive comparison of Aerosol Comparisons between Observations and Models (AeroCom) project model simulations with satellite observations. A total of 59 runs by 18 models from three AeroCom Phase-III experiments (i.e., biomass burning emissions, CTRL16, and CTRL19) and 14 satellite products of aerosols were used in the study. Aerosol optical depth (AOD) at 550 nm was investigated during the fire season over three key fire regions reflecting different fire dynamics (i.e., deforestation-dominated Amazon, Southern Hemisphere Africa where savannas are the key source of emissions, and boreal forest burning in boreal North America). The 14 satellite products were first evaluated against AErosol RObotic NETwork (AERONET) observations, with large uncertainties found. But these uncertainties had small impacts on the model evaluation that was dominated by modeling bias. Through a comparison with Polarization and Directionality of the Earth's Reflectances measurements with the Generalized Retrieval of Aerosol and Surface Properties algorithm (POLDER-GRASP), we found that the modeled AOD values were biased by -93 % to 152 %, with most models showing significant underestimations even for the state-of-the-art aerosol modeling techniques (i.e., CTRL19). By scaling up BBA emissions, the negative biases in modeled AOD were significantly mitigated, although it yielded only negligible improvements in the correlation between models and observations, and the spatial and temporal variations in AOD biases did not change much. For models in CTRL16 and CTRL19, the large diversity in modeled AOD was in almost equal measures caused by diversity in emissions, lifetime, and the mass extinction coefficient (MEC). We found that in the AeroCom ensemble, BBA lifetime correlated significantly with particle deposition (as expected) and in turn correlated strongly with precipitation. Additional analysis based on Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) aerosol profiles suggested that the altitude of the aerosol layer in the current models was generally too low, which also contributed to the bias in modeled lifetime. Modeled MECs exhibited significant correlations with the Ångström exponent (AE, an indicator of particle size). Comparisons with the POLDER-GRASP-observed AE suggested that the models tended to overestimate the AE (underestimated particle size), indicating a possible underestimation of MECs in models. The hygroscopic growth in most models generally agreed with observations and might not explain the overall underestimation of modeled AOD. Our results imply that current global models contain biases in important aerosol processes for BBA (e.g., emissions, removal, and optical properties) that remain to be addressed in future research. [ABSTRACT FROM AUTHOR]

Aims and Scope This Special Issue aims to better understand the response of air quality to climate change and environmental/climate policies from a global perspective. Air pollution is one of the most concerning environmental threats to human health. By using various techniques (including routine monitoring networks, city campaigns, and modeling), these studies discuss how air quality responds to climate/meteorological changes and emission mitigation policies. [Extracted from the article]

Fire forecasts that predict dry‐season fire activities several months in advance are beneficial for fire management. On a global scale, however, the predictability of fires is limited because fires depend on multiple factors and lack a single dominant predictor to describe diverse fire characteristics across regions. Here, based on 33 local meteorological parameters (MPs) and 37 large‐scale climate indices (CIs), we establish four empirical model clusters to predict global interannual fire variability. We show that across various geographic locations, the models provide reliable fire forecasts at least three months prior to the peak fire months. Compared to MPs, CIs such as the Oceanic Niño Index are comparable or even superior predictors. Globally, as well as in most continents, the El Niño–Southern Oscillation is the major driving force, explaining 17% of interannual fire variability, with strong implications for fire carbon emissions and the global carbon cycle. Other important predictors include the Northern Atlantic sea surface temperature (9%), the Southern Atlantic sea surface temperature (5%), and the Pacific/North American Pattern (3%). The predictive models reveal a strong interaction between MPs and CIs, indicating potential climate‐induced modification of fire responses to meteorological conditions. We show that the newly developed predictive models can benefit future fire management in response to climate change. Plain Language Summary: Integration of climate indices and meteorological parameters enables more accurate fire forecasts globally with a longer forecast length. Key Points: We developed empirical models to provide global fire forecasts 3 months prior to the peak fire monthsCombining meteorological and climate variables enhances fire predictabilityThe El Niño–Southern Oscillation is a driving factor that affects global interannual fire variability [ABSTRACT FROM AUTHOR]

A novel enhanced diffusion sampler for collecting gaseous phase polycyclic aromatic hydrocarbons (PAHs) without air agitation is proposed. The diffusion of target compounds into a sampling chamber is facilitated by continuously purging through a closed-loop flow to create a large concentration difference between the ambient air and the air in the sampling chamber. A glass-fiber filter-based prototype was developed. It was demonstrated that the device could collect gaseous PAHs at a much higher rate (1.6 ± 1.4 L/min) than regular passive samplers, while the ambient air is not agitated. The prototype was also tested in both the laboratory and field for characterizing the concentration gradients over a short distance from the soil surface. The sampler has potential to be applied in other similar situations to characterize the concentration profiles of other chemicals. [ABSTRACT FROM AUTHOR]

There is increasing evidence indicating the critical role of ammonia (NH3) in the formation of secondary aerosols. Therefore, high quality NH3 emission inventory is important for modeling particulate matter in the atmosphere. Unfortunately, without directly measured emission factors (EFs) in developing countries, using data from developed countries could result in an underestimation of these emissions. A series of newly reported EFs for China provide an opportunity to update the NH3 emission inventory. In addition, a recently released fuel consumption data product has allowed for a multisource, high-resolution inventory to be assembled. In this study, an improved global NH3 emission inventory for combustion and industrial sources with high sectorial (70 sources), spatial (0.1° × 0.1°), and temporal (monthly) resolutions was compiled for the years 1960 to 2013. The estimated emissions from transportation (1.59 Tg) sectors in 2010 was 2.2 times higher than those of previous reports. The spatial variation of the emissions was associated with population, gross domestic production, and temperature. Unlike other major air pollutants, NH3 emissions continue to increase, even in developed countries, which is likely caused by an increased use of biomass fuel in the residential sector. The emissions density of NH3 in urban areas is an order of magnitude higher than in rural areas. [ABSTRACT FROM AUTHOR]

The quantification of carbon monoxide (CO) emissions is necessary for atmospheric research and has been studied extensively. Aiming to build an inventory with both high spatial resolution and detailed source information, this study estimated the monthly nation-level CO emissions from 79 major sources from 1960 to 2013, based on which a 0.1° × 0.1° gridded emission map was developed for 2011 using a recent energy product. The high sectorial resolution of this inventory can help scientists to study the influence of socioeconomic development on emissions, help decision makers to formulate abatement strategies, and potentially benefit emission-reduction scenario modeling and cost-benefit analysis. Our estimate for 2011 was 888.17 Tg (745.67 Tg-1112.80 Tg), with a much higher contribution from anthropogenic activities (68 %) than wildfire and deforestation (32 %). The anthropogenic emissions in recent years were dominated by developing countries due to the continuously increasing industrial production intensity and/or population explosion. Further discussion of the spatial and temporal variation of emissions was conducted, and a decreased emission intensity was observed, which was attributed to related policies and technological progress. [ABSTRACT FROM AUTHOR]

Wildland fires constitute a major source of ambient fine particulate matter (PM 2.5 ), significantly impacting air quality and public health. As the climate becomes warmer and drier, fire frequency is projected to rise, yet how the associated health impacts of fire-sourced PM 2.5 (FPM 2.5 ) respond to climate change remains vague. In this study, we modeled the global concentration and associated premature deaths of FPM 2.5 over the past two decades. Our results reveal an upward trend in FPM 2.5 concentrations globally, contributing to an increase in premature deaths from 156,000 to 241,000 between 2000 and 2021, alongside population growth and aging. Substantial variations are observed on a regional scale, but most regions experience increasing exposure levels, suggesting an increased impact of FPM 2.5 . Further regression analysis indicates that climate change plays a critical role in these trends, accounting for 56% of the net changes in FPM 2.5 -related premature deaths. Warming and drought emerge as key drivers of climate-induced health risks, with slightly different contributions to the FPM 2.5 concentration and the associated premature deaths. Our findings underscore the urgent need to integrate climate adaptation strategies with fire management to mitigate the growing health burden caused by FPM 2.5 worldwide.

Absorbing aerosols emitted from biomass burning (BB) greatly affect the radiation balance, cloudiness, and circulation over tropical regions. Assessments of these impacts rely heavily on the modeled aerosol absorption from poorly constrained global models and thus exhibit large uncertainties. By combining the AeroCom model ensemble with satellite and in situ observations, we provide constraints on the aerosol absorption optical depth (AAOD) over the Amazon and Africa. Our approach enables identification of error contributions from emission, lifetime, and MAC (mass absorption coefficient) per model, with MAC and emission dominating the AAOD errors over Amazon and Africa, respectively. In addition to primary emissions, our analysis suggests substantial formation of secondary organic aerosols over the Amazon but not over Africa. Furthermore, we find that differences in direct aerosol radiative effects between models decrease by threefold over the BB source and outflow regions after correcting the identified errors. This highlights the potential to greatly reduce the uncertainty in the most uncertain radiative forcing agent.

Gaseous and particulate chlorine species play an important role in modulating tropospheric oxidation capacity, aerosol water uptake, visibility degradation, and human health. The lack of recent global continental chlorine emissions has hindered modeling studies of the role of chlorine in the atmosphere. Here, we develop a comprehensive global emission inventory of gaseous HCl and particulate Cl - (pCl), including 35 sources categorized in six source sectors based on published up-to-date activity data and emission factors. These emissions are gridded at a spatial resolution of 0.1° × 0.1° for the years 1960 to 2014. The estimated emissions of HCl and pCl in 2014 are 2354 (1661-3201) and 2321 (930-3264) Gg Cl a -1 , respectively. Emissions of HCl are mostly from open waste burning (38%), open biomass burning (19%), energy (19%), and residential (13%) sectors, and the major sources classified by fuel type are combustion of waste (43%), biomass (32%), and coal (25%). Emissions of pCl are mostly from biofuel (29%) and open biomass burning processes (44%). The sectoral and spatial distributions of HCl and pCl emissions are very heterogeneous along the study period, and the temporal trends are mainly driven by the changes in emission factors, energy intensity, economy, and population.

Atmospheric black carbon (BC) concentrations are governed by both emissions and meteorological conditions. Distinguishing these effects enables quantification of the effectiveness of emission mitigation actions by excluding meteorological effects. Here, we develop reduced-form models in both direct (RFDMs) and inverse (RFIMs) modes to estimate ambient BC concentrations. The models were developed based on outputs from multiyear simulations under three conditional scenarios with realistic or fixed emissions and meteorological conditions. We established a set of probabilistic functions (PFs) to quantify the meteorological influences. A significant two-way linear relationship between multiyear annual emissions and mean ambient BC concentrations was revealed at the grid cell scale. The correlation between them was more significant at grid cells with high emission densities. The concentrations and emissions at a given grid cell are also significantly correlated with emissions and concentrations of the surrounding areas, respectively, although to a lesser extent. These dependences are anisotropic depending on the prevailing winds and source regions. The meteorologically induced variation at the monthly scale was significantly higher than that at the annual scale. Of the major meteorological parameters, wind vectors, temperature, and relative humidity were found to most significantly affect variation in ambient BC concentrations.

Residential contribution to air pollution-associated health impacts is critical, but inadequately addressed because of data gaps. Here, we fully model the effects of residential energy use on emissions, outdoor and indoor PM 2.5 concentrations, exposure, and premature deaths using updated energy data. We show that the residential sector contributed only 7.5% of total energy consumption but contributed 27% of primary PM 2.5 emissions; 23 and 71% of the outdoor and indoor PM 2.5 concentrations, respectively; 68% of PM 2.5 exposure; and 67% of PM 2.5 -induced premature deaths in 2014 in China, with a progressive order of magnitude increase from sources to receptors. Biomass fuels and coal provided similar contributions to health impacts. These findings are particularly true for rural populations, which contribute more to emissions and face higher premature death risks than urban populations. The impacts of both residential and nonresidential emissions are interconnected, and efforts are necessary to simultaneously mitigate both emission types., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).)

The presence of sulfur dioxide (SO 2 ) in the air is a global concern because of its severe environmental and public health impacts. Recent evidence from satellite observations shows rapid changes in the spatial distribution of global SO 2 emissions, but such features are generally missing in global emission inventories that use a bottom-up method due to the lack of up-to-date information, especially in developing countries. Here, we rely on the latest data available on emission activities, control measures, and emission factors to estimate global SO 2 emissions for the period 1960-2014 on a 0.1° × 0.1° spatial resolution. We design two counterfactual scenarios to isolate the contributions of emission activity growth and control measure deployment on historical SO 2 emission changes. We find that activity growth has been the major factor driving global SO 2 emission changes overall, but control measure deployment is playing an increasingly important role. With effective control measures deployed in developed countries, the predominant emission contributor has shifted from developed countries in the early 1960s (61%) to developing countries at present (83%). Developing countries show divergency in mitigation strategies and thus in SO 2 emission trends. Stringent controls in China are driving the recent decline in global emissions. A further reduction in SO 2 emissions would come from a large number of developing nations that currently lack effective SO 2 emission controls.

Historically, beehive coke ovens (BCOs) were extensively operated in China and emitted large quantities of pollutants, including primary PM 2.5 and secondary PM 2.5 precursors, and other climate forcers. Although these ovens were legally banned in 1996 by the Coal Law, the process of phasing them out took over a decade to accomplish. Based on historical operation data derived from remote sensing images, temporal trends and the spatial distribution of the emissions of various pollutants from BCOs were compiled and used to model the resulting perturbation in ambient PM 2.5 , population exposure, and PM 2.5 -associated adverse health impacts. Historically, PM 2.5 originating from BCOs affected a vast region across China, which peaked in approximately 1996 and decreased afterward until the ovens' final elimination in 2011. According to the results of a supply-demand model, emissions from the BCOs would have continued to increase after 1996 if they had not been banned. As a result, national average PM 2.5 attributable to BCOs in 2014 would have been more than three times as high as that in 1996. It was estimated that the cumulative number of premature deaths associated with BCO-originating PM 2.5 from 1982 to 2014 was as high as 365 000 (95% confidence interval 259 000-402 000). The number would have nearly tripled if BCOs had not been banned and halved if the ban had been implemented immediately after the regulation was in force, suggesting the importance of legislation implementation.

Although PM 2.5 (particulate matter with aerodynamic diameters less than 2.5 μm) in the air originates from emissions, its concentrations are often affected by confounding meteorological effects. Therefore, direct comparisons of PM 2.5 concentrations made across two periods, which are commonly used by environmental protection administrations to measure the effectiveness of mitigation efforts, can be misleading. Here, we developed a two-step method to distinguish the significance of emissions and meteorological factors and assess the effectiveness of emission mitigation efforts. We modeled ambient PM 2.5 concentrations from 1980 to 2014 based on three conditional scenarios: realistic conditions, fixed emissions, and fixed meteorology. The differences found between the model outputs were analyzed to quantify the relative contributions of emissions and meteorological factors. Emission-related gridded PM 2.5 concentrations excluding the meteorological effects were predicted using multivariate regression models, whereas meteorological confounding effects on PM 2.5 fluctuations were characterized by probabilistic functions. When the regression models and probabilistic functions were combined, fluctuations in the PM 2.5 concentrations induced by emissions and meteorological factors were quantified for all model grid cells and regions. The method was then applied to assess the historical and future trends of PM 2.5 concentrations and potential fluctuations on global, national, and city scales. The proposed method may thus be used to assess the effectiveness of mitigation actions.

Direct residential and transportation energy consumption (RTC) contributes significantly to ambient fine particulate matter with a diameter smaller than 2.5 μm (PM 2.5 ) in China. During massive rural-urban migration, population and pollutant emissions from RTC have evolved in terms of magnitude and geographic distribution, which was thought to worsen PM 2.5 levels in cities but has not been quantitatively addressed. We quantify the temporal trends and spatial patterns of migration to cities and evaluate their associated pollutant emissions from RTC and subsequent health impact from 1980 to 2030. We show that, despite increased urban RTC emissions due to migration, the net effect of migration in China has been a reduction of PM 2.5 exposure, primarily because of an unequal distribution of RTC energy mixes between urban and rural areas. After migration, people have switched to cleaner fuel types, which considerably lessened regional emissions. Consequently, the national average PM 2.5 exposure concentration in 2010 was reduced by 3.9 μg/m 3 (90% confidence interval, 3.0 to 5.4 μg/m 3 ) due to migration, corresponding to an annual reduction of 36,000 (19,000 to 47,000) premature deaths. This reduction was the result of an increase in deaths by 142,000 (78,000 to 181,000) due to migrants swarming into cities and decreases in deaths by 148,000 (76,000 to 194,000) and 29,000 (15,000 to 39,000) due to transitions to a cleaner energy mix and lower urban population densities, respectively. Locally, however, megacities such as Beijing and Shanghai experienced increases in PM 2.5 exposure associated with migration because these cities received massive immigration, which has driven a large increase in local emissions.

Emission quantification of primary particulate matter (PM) is essential for assessment of its related climate and health impacts. To reduce uncertainty associated with global emissions of PM2.5, PM10, and TSP, we compiled data with high spatial (0.1° × 0.1°) and sectorial (77 primary sources) resolutions for 2007 based on a newly released global fuel data product (PKU-FUEL-2007) and an emission factor database. Our estimates for developing countries are higher than those previously reported. Spatial bias associated with large countries could be reduced by using subnational fuel consumption data. Additionally, we looked at temporal trends from 1960 to 2009 at country-scale resolution. Although total emissions are still increasing in developing countries, their intensities in terms of gross domestic production or energy consumption have decreased. PM emitted in developed countries is finer owing to a larger contribution from nonindustrial sources and use of abatement technologies. In contrast, countries like China, with strong industry emissions and limited abatement facilities, emit coarser PM. The health impacts of PM are intensified in hotspots and cities owing to covariance of sources and receptors. Although urbanization reduces the per person emission, overall health impacts related to these emissions are heightened because of aggregation effects.