A Physically Constrained Deep-Learning Fusion Method for Estimating Surface NO Concentration from Satellite and Ground Monitors.

Accurate estimation of atmospheric chemical concentrations from multiple observations is crucial for assessing the health effects of air pollution. However, existing methods are limited by imbalanced samples from observations. Here, we introduce a novel deep-learning model-measurement fusion method (DeepMMF) constrained by physical laws inferred from a chemical transport model (CTM) to estimate NO concentrations over the Continental United States (CONUS).

Regional background ozone estimation for China through data fusion of observation and simulation.

Regional background ozone (O_) is an important component of surface ozone (O). However, due to the uncertainties in commonly used Chemical Transport Models (CTMs) and statistical models, accurately assessing O_ in China is challenging. In this study, we calculated the O_ concentrations with the CTM - Brute Force Method (BFM) and constrained the results with site observations of O with the multiple linear regression (MLR) model.

Ozone response modeling to NOx and VOC emissions: Examining machine learning models.

Current machine learning (ML) applications in atmospheric science focus on forecasting and bias correction for numerical modeling estimations, but few studies examined the nonlinear response of their predictions to precursor emissions. This study uses ground-level maximum daily 8-hour ozone average (MDA8 O) as an example to examine O responses to local anthropogenic NOx and VOC emissions in Taiwan by Response Surface Modeling (RSM). Three different datasets for RSM were examined, including the Community Multiscale Air Quality (CMAQ) model data, ML-measurement-model fusion (ML-MMF) data, and ML data, which respectively represent direct numerical model predictions, numerical predictions adjusted by observations and other auxiliary data, and ML predictions based on observations and other auxiliary data.

Quantifying the wet deposition of reactive nitrogen over China: Synthesis of observations and models.

Accurate estimation on reaction nitrogen (N) deposition is highly demanded for assessing the impacts on the environment and human beings. This study investigated the wet deposition of inorganic nitrogen (IN) in mainland China by measurements from over 500 sites from five observational networks/databases and ensemble results of eleven chemical transport models (CTMs). Each data source has its focus and limitations and together formed a comprehensive view over China.

Understanding the role of anthropogenic emissions in glaciers retreat in the central Andes of Chile.

Glaciers in Chilean Central Andes have significatively retreated, at least, in the last 60 years. From 2004 to 2014, the largest retreat in the area (-0.15 km yr) was observed at Olivares Alpha Glacier (OAG).

Localized energy burden, concentrated disadvantage, and the feminization of energy poverty.

Energy burden directly influences households' health and safety. Amid a growing literature on energy, poverty and gender remains relatively understudied. We evaluate socioeconomic, geographic, and health factors as multidimensions of concentrated disadvantage that magnify energy burden in the United States over time.

Projections of future wildfires impacts on air pollutants and air toxics in a changing climate over the western United States.

Wildfires emit smoke particles and gaseous pollutants that greatly aggravate air quality and cause adverse health impacts in the western US (WUS). This study evaluates how wildfire impacts on air pollutants and air toxics evolve from the present climate to the future climate under a high anthropogenic emission scenario at regional and city scales. Through employing multiple climate and chemical transport models, small changes in domain-averaged air pollutant concentrations by wildfires are simulated over WUS.

Improving Estimates of Sulfur, Nitrogen, and Ozone Total Deposition through Multi-Model and Measurement-Model Fusion Approaches.

Earth system and environmental impact studies need high quality and up-to-date estimates of atmospheric deposition. This study demonstrates the methodological benefits of multimodel ensemble and measurement-model fusion mapping approaches for atmospheric deposition focusing on 2010, a year for which several studies were conducted. Global model-only deposition assessment can be further improved by integrating new model-measurement techniques, including expanded capabilities of satellite observations of atmospheric composition.

The black carbon dispersion in the Southern Hemisphere and its transport and fate to Antarctica, an Anthropocene evidence for climate change policies.

Black carbon (BC) has been measured in Antarctica's air, and its global warming effect can potentially speed up the ice melting in the most solid water reservoir of the planet. However, the primary responsible sources are not well evidenced in this region. The dispersion of black carbon emissions from the Southern Hemisphere was conducting using atmospheric chemical transport model and we compared the results with satellite registries from March 1st to April 30th in 2014.

Quantifying spatial heterogeneity of vulnerability to short-term PM exposure with data fusion framework.

The current estimations of the burden of disease (BD) of PM exposure is still potentially biased by two factors: ignorance of heterogeneous vulnerabilities at diverse urbanization levels and reliance on the risk estimates from existing literature, usually from different locations. Our objectives are (1) to build up a data fusion framework to estimate the burden of PM exposure while evaluating local risks simultaneously and (2) to quantify their spatial heterogeneity, relationship to land-use characteristics, and derived uncertainties when calculating the disease burdens. The feature of this study is applying six local databases to extract PM exposure risk and the BD information, including the risks of death, cardiovascular disease (CVD), and respiratory disease (RD), and their spatial heterogeneities through our data fusion framework.

Predicting U.S. Residential Building Energy Use and Indoor Pollutant Exposures in the Mid-21st Century.

The extent to which climate change and other factors will influence building energy use and population exposures to indoor pollutants is not well understood. Here, we develop and apply nationally representative residential energy and indoor pollutant model sets to estimate energy use, indoor pollutant concentrations, and associated chronic health outcomes across the U.S.

Evaluating the impact of mobility on COVID-19 pandemic with machine learning hybrid predictions.

COVID-19 pandemic had expanded to the US since early 2020 and has caused nationwide economic loss and public health crisis. Until now, although the US has the most confirmed cases in the world and are still experiencing an increasing pandemic, several states insisted to re-open business activities and colleges while announced strict control measures. To provide a quantitative reference for official strategies, predicting the near future trend based on finer spatial resolution data and presumed scenarios are urgently needed.

Transient risk of ambient fine particulate matter on hourly cardiovascular events in Tainan City, Taiwan.

Background: The association between daily changes in ambient fine particulate matter (PM2.5) and cardiovascular diseases have been well established in mechanistic, epidemiologic and exposure studies. Only a few studies examined the effect of hourly variations in air pollution on triggering cardiovascular events.

The climate impact on atmospheric stagnation and capability of stagnation indices in elucidating the haze events over North China Plain and Northeast China.

In this study, the spatial pattern and temporal evolution of PM over North China Plain (NCP) and Northeast China (NEC) during 2014-2018 was investigated. The annual mean PM shows clear decreasing trends over time, but the seasonal mean PM as well as the seasonal total duration and frequency of haze days shows large inter-annual fluctuation. Based on the atmospheric stagnation index (ASI), this study examined the correlation between ASI and haze events over NCP and NEC.

Ammonia emission abatement does not fully control reduced forms of nitrogen deposition.

Human activities and population growth have increased the natural burden of reactive nitrogen (N) in the environment. Excessive N deposition on Earth's surface leads to adverse feedbacks on ecosystems and humans. Similar to that of air pollution, emission control is recognized as an efficient means to control acid deposition.

Satellite-Based Daily PM Estimates During Fire Seasons in Colorado.

The western United States has experienced increasing wildfire activities, which have negative effects on human health. Epidemiological studies on fine particulate matter (PM) from wildfires are limited by the lack of accurate high-resolution PM exposure data over fire days. Satellite-based aerosol optical depth (AOD) data can provide additional information in ground PM concentrations and has been widely used in previous studies.

Nonlinear relationships between air pollutant emissions and PM-related health impacts in the Beijing-Tianjin-Hebei region.

A direct and quantitative linkage of air pollution-related health effects to emissions from different sources is critically important for decision-making. While a number of studies have attributed the PM-related health impacts to emission sources, they have seldom examined the complicated nonlinear relationships between them. Here we investigate the nonlinear relationships between PM-related premature mortality in the Beijing-Tianjin-Hebei (BTH) region, one of the most polluted regions in the world, and emissions of different pollutants from multiple sectors and regions, through a combination of chemical transport model (CTM), extended response surface model (ERSM), and concentration-response functions (CRFs).

Spatial variation of modelled total, dry and wet nitrogen deposition to forests at global scale.

Forests are an important biome that covers about one third of the global land surface and provides important ecosystem services. Since atmospheric deposition of nitrogen (N) can have both beneficial and deleterious effects, it is important to quantify the amount of N deposition to forest ecosystems. Measurements of N deposition to the numerous forest biomes across the globe are scarce, so chemical transport models are often used to provide estimates of atmospheric N inputs to these ecosystems.

Modeling cold soak evaporative vapor emissions from gasoline-powered automobiles using a newly developed method.

Volatile organic compounds (VOCs) evaporate and vent from a vehicle's fuel tank to its evaporative control system when the vehicle is both driven and parked. VOCs making it past the control system are emissions. Driving and parking activity, fuel volatility, and temperature strongly affect vapor generation and the effectiveness of control technologies, and the wide variability in these factors and the sensitivity of emissions to these factors make it difficult to estimate evaporative emissions at the macro level.

Uncertainty Quantification of Extratropical Forest Biomass in CMIP5 Models over the Northern Hemisphere.

Simplified representations of processes influencing forest biomass in Earth system models (ESMs) contribute to large uncertainty in projections. We evaluate forest biomass from eight ESMs outputs archived in the Coupled Model Intercomparison Project Phase 5 (CMIP5) using the biomass data synthesized from radar remote sensing and ground-based observations across northern extratropical latitudes. ESMs exhibit large biases in the forest distribution, forest fraction, and mass of carbon pools that contribute to uncertainty in forest total biomass (biases range from -20 Pg C to 135 Pg C).

Analysis of the Co-existence of Long-range Transport Biomass Burning and Dust in the Subtropical West Pacific Region.

Biomass burning and wind-blown dust has been well investigated during the past decade regarding their impacts on environment, but their co-existence hasn't been recognized because they usually occur in different locations and episodes. In this study we reveal the unique co-existence condition that dust from the Taklamakan and Gobi Desert (TGD) and biomass burning from Peninsular Southeast Asia (PSEA) can reach to the west Pacific region simultaneously in boreal spring (March and April). The upper level trough at 700hPa along east coast of China favors the large scale subsidence of TGD dust while it travels southeastwards, and drives the PSEA biomass burning plume carried by the westerlies at 3-5 km to descend rapidly to around 1.

Energy consumption and energy-saving potential analysis of pollutant abatement systems in a 1000-MW coal-fired power plant.

Unlabelled: Pollutant abatement systems are widely applied in the coal-fired power sector, and the energy consumption is considered an important part of the auxiliary power. An energy consumption analysis and assessment model of pollutant abatement systems in a power unit was developed based on the dynamic parameters and technology. The energy consumption of pollutant abatement systems in a 1000-MW coal-fired power unit that meets the ultra-low emission limits and the factors of operating parameters, including unit load and inlet concentration of pollutants, on the operating power were analyzed.

First long-term detection of paleo-oceanic signature of dust aerosol at the southern marginal area of the Taklimakan Desert.

We firstly conducted a long-term in-situ field measurement at a marginal area (Hotan) of the southern Taklimakan Desert covering all four seasons. Detailed chemical characterization of dust aerosol over Hotan showed several unconventional features, including (1) ubiquity of high Na and Cl abundances in the Taklimakan dust aerosol and its Cl/Na ratio close to seawater; (2) high Ca content in the Taklimakan dust (7.4~8.

The impact of climate change and emissions control on future ozone levels: Implications for human health.

Overwhelming evidence has shown that, from the Industrial Revolution to the present, human activities influence ground-level ozone (O) concentrations. Past studies demonstrate links between O exposure and health. However, knowledge gaps remain in our understanding concerning the impacts of climate change mitigation policies on O concentrations and health.