Variable and dynamic associations between hot weather, thermal comfort, and individuals' emotional states during summertime

The effects of heat exposure on negative affect are thought to be central to the observed relationships between hot summer days and deleterious outcomes, such as violent crime or mental health crises. As these relationships are likely to be magnified by the effects of climate change, a better understanding of how consistent or variable the effects of hot weather on affective states is required. The current work combines data gathered from an ecological momentary assessment (EMA) study on individuals' thermal perceptions, comfort, and affective states in outdoor environments during their daily lives with high spatiotemporal resolution climate-modeled weather variables. Using these data, associations between objective weather variables (temperature, humidity, etc.), perceived heat (thermal perception and comfort), and affective states are examined. Overall, objective weather data reasonably predicted perception and comfort, but only comfort predicted negative affective states. The variance explained across individuals was generally very low in predicting negative affect or comfort, but within-person variance explained was high. In other words, while there may be a relatively consistent relationship between temperature and psychological experience for any given person, there are significant individual differences across people. Age and gender were examined as moderators of these relationships, and while gender had no impact, participant age showed several significant interactions. Specifically, while older adults tended to experience more thermal discomfort and perceived higher temperatures as hotter, the relationship between discomfort and negative affect was lower in older adults. Taken together, these results emphasize the importance of thermal discomfort specifically in predicting negative affect, as well as the high inter-individual variability in thermal perceptions and comfort for the same ambient temperatures.

Assessing urban heat island mitigation potential of realistic roof greening across local climate zones: A highly-resolved weather research and forecasting model study

Given their multifold benefits, green roofs are often considered to mitigate the urban heat island (UHI) effect. Most mesoscale studies consider 100 % green roof fraction or the same green roof fraction in each urban land use category while analysing the influence of green roofs on the UHI effect, which can overestimate their impact on UHI. Consequently, the impact of green roofs evaluated in these studies may not be suitable for informing policy decisions. Furthermore, the effect of morphologies on temperature reduction due to green roofs has not been previously studied. To address this gap, in this paper, we evaluate the impact of a realistic fraction of green roofs specific to the respective local climate zones (LCZ) on the UHI effect during a heatwave in Liège, Belgium, employing a high-resolution WRF study using the BEP-BEM parameterisation with LCZ land use classification. The realistic fraction is estimated for every LCZ class based on the average percentage of flat roofs observed in each LCZ class in Liège. Accordingly, distinct realistic fractions of green roofs are assigned to each LCZ class in WRF. We run the WRF simulation for the base scenario (without green roofs), extreme scenario (100 % green roof fraction), and realistic scenario. The results indicate a limited reduction in near-surface air and surface temperature in a realistic scenario, with a nighttime increase in temperature. Additionally, in the extreme scenario, the temperature reduction largely depends on the morphology. However, in a realistic scenario, it depends on the green roof fraction. Other indicators like heat index and UHI intensity also are not reduced considerably with realistic greening. Therefore, realistic roof greening alone will not be sufficient to achieve an impact on a city-scale.

Influence of biomass burning on ozone levels in the Megalopolis of Central Mexico during the COVID-19 lockdown

The massive reductions in anthropogenic emissions resulting from the COVID-19 lockdown provided a unique opportunity to evaluate the effect of mitigation measures aiming to abate air pollution. In Mexico, the total lockdown period took place during the dry-hot season when biomass burning activity is enhanced. Here, we investigate the role of biomass burning emissions on regional ozone levels in the Megalopolis of Central Mexico. The studied period covers the lockdown phases 2 and 3, and the first month of the New Normal. We applied a factor separation technique and process analysis to estimate the pure and synergistic contributions of emission reductions under lockdown and that from biomass burning to daily ozone maximum concentrations in 7 metropolitan areas of different states in the Megalopolis. The results revealed that biomass burning plumes likely masked the effect of massive reductions from mobile emissions, impacted the PBL development during phase 3 and favored transition and mixed NOx-limited and VOC-limited regional regimes. This contributed to increased ozone production in the middle to lower PBL by changing the regional background levels which potentially could bias high ozone production efficiency estimations. Given the Megalopolis contribution to economic and societal development at national scale, our study suggests that ozone mitigation measures during the dry-hot season targeting mainly mobile emissions will likely be offset by biomass burning plumes. A regional and synergic policy aiming to control biomass burning would help to reduce the occurrence of high ozone levels in Central Mexico with the co-benefit of tackling short-lived climate pollutants.

Effect of microphysics scheme and data assimilation on hydrometeor and radiative flux simulations in the Arctic

Although clouds are a major factor influencing atmospheric environments in the Arctic, numerical simulations of Arctic clouds are uncertain. In this study, the effects of microphysics scheme and data assimilation (DA) on the simulation of clouds, hydrometeors and radiative fluxes in the Arctic were investigated using the polar weather research and forecasting (WRF) model and three-dimensional variational DA. Compared with the WRF 5-class (WSM5) microphysics scheme, when the Morrison double-moment (Morrison) scheme was used, the simulated amount of cloud ice water decreased by approximately 68%. In contrast, the amount of water vapour, cloud liquid water, snow and rain in the atmosphere increased. With DA, the amount of water vapour increased, leading to increased hydrometeors. The cloud liquid water increased in the middle and low atmospheres when Morrison was used, whereas it increased in the low atmosphere when DA was used. The increase in cloud liquid water by using Morrison resulted in a decrease in the downward short-wave radiative flux at the surface, whereas using DA increased the downward long-wave radiative flux. Changing the microphysics scheme induced redistribution of the region and amounts of hydrometeors, whereas DA induced an increase in hydrometeors in specific regions by adding observation information to the model states.

Evaluation of WRF-Chem air quality forecasts during the AEROMMA and STAQS 2023 field campaigns

A real-time air quality forecasting system was developed using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to provide support for flight planning activities during the NOAA Atmospheric Emissions and Reactions Observed from Megacities to Marine Areas (AEROMMA) and NASA Synergistic TEMPO Air Quality Science (STAQS) 2023 field campaigns. The forecasting system operated on two separate domains centered on Chicago, IL, and New York City, NY, and provided 72-hour predictions of atmospheric composition, aerosols, and clouds. This study evaluates the Chicago-centered forecasting system's 1-, 2-, and 3-day ozone (O3) forecast skill for Chiwaukee Prairie, WI, a rural area downwind of Chicago that often experiences high levels of O3 pollution. Comparisons to vertical O3 profiles collected by a Tropospheric Ozone Lidar Network (TOLNet) instrument revealed that forecast skill decreases as forecast lead time increases. When compared to surface measurements, the forecasting system tended to underestimate O3 concentrations on high O3 days and overestimate on low O3 days at Chiwaukee Prairie regardless of forecast lead time. Using July 25, 2023, as a case study, analyses show that the forecasts underestimated peak O3 levels at Chiwaukee Prairie during this regionwide bad air quality day. Wind speed and direction data indicates that this underestimation can partially be attributed to lake breeze simulation errors. Surface fine particulate matter (PM2.5) measurements, Geostationary Operational Environmental Satellite-16 (GOES-16) aerosol optical depth (AOD) data, and back trajectories from the NOAA Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model show that transported Canadian wildfire smoke impacted the Lake Michigan region on this day. Errors in the forecasted chemical composition and transport of the smoke plumes also contributed to underpredictions of O3 levels at Chiwaukee Prairie on July 25, 2023. The results of this work help identify improvements that can be made for future iterations of the WRF-Chem forecasting system.Implications: Air quality forecasting is an important tool that can be used to inform the public about upcoming high pollution days so that individuals may plan accordingly to limit their exposure to health-damaging air pollutants. Forecasting also helps scientists make decisions about where to make observations during air quality field campaigns. A variety of observational datasets were used to evaluate the accuracy of an air quality forecasting system that was developed for NOAA and NASA field campaigns that occurred in the summer of 2023. These evaluations inform areas of improvement for future development of this air quality forecasting system.

Impact of air quality model settings for the evaluation of emission reduction strategies to curb air pollution

For air quality management, while numerical tools are mainly evaluated to assess their performances on absolute concentrations, this study assesses the impact of their settings on the robustness of model responses to emission reduction strategies for the main criteria pollutants. The effect of the spatial resolution and chemistry schemes is investigated. We show that whereas the spatial resolution is not a crucial setting (except for NO2), the chemistry scheme has more impact, particularly when assessing hourly values of the absolute potential of concentrations. The analysis of model responses under the various configurations triggered an analysis of the impact of using online models, like WRF-chem or WRF-CHIMERE, which accounts for the impact of aerosol concentrations on meteorology. This study informs the air quality modeling community on what extent some model settings can affect the expected model responses to emission changes. We suggest to not activate online effects when analyzing the effect of an emission reduction strategy to avoid any confusion in the interpretation of results even if an online simulation should represent better the reality.

Modifications on the coastal atmospheric sulfur and cloud condensation nuclei along the Eastern China seas by shipping fuel transition

Marine fuel combustion from shipping releases SO2 and forms sulfate particles, which may alter low cloud characteristics. A series of strategies were implemented to control the sulfur content of ship fuel oil from 2018 to 2020, offering insights into the effects of the ship fuel oil transition on sulfur-related pollutants and the consequent cloud condensation nuclei (CCN) in the atmosphere. Compared to 2018 in the southeast China waters, shipping SO2 emission decreased by 78 % in 2020, resulting in a 76 % reduction in ship-related total sulfur concentration, and a decrease of 54 % in CCN number concentration under supersaturation 0.2 % (CCN0.2) contributed by shipping. The response of CCN0.2 to ship-related sulfate modification is more pronounced in relatively clean environments than polluted environments, highlighting the uneven changes in coastal CCN along the Eastern China Sea induced by the ship fuel policies. CCN can trigger the formation of cloud droplets, 2020 fuel regulation may have and will reduce the cooling radiative forcing effect with strong spatial heterogeneity. The study provides insights into the variations in coastal atmospheric sulfur-related pollutants and CCN in uneven response to changes in ship fuel oil, prompting the need for further comprehensive assessments of the climate effects resulting from potential shifts in ship fuel use in the future.

Smoke-charged vortex doubles hemispheric aerosol in the middle stratosphere and buffers ozone depletion

Australian mega-wildfires in the summer of 2019-2020 injected smoke into the stratosphere, causing strong ozone depletion in the lower stratosphere. Here, we model the smoke plume and reproduce its unexpected trajectory toward the middle stratosphere at ~35-kilometer altitude. We show that a smoke-charged vortex (SCV) induced and maintained by absorbing aerosols played a key role in lofting pollutants from the lower stratosphere and nearly doubled the southern hemispheric aerosol burden in the middle stratosphere. The SCV caused a redistribution of stratospheric aerosols, which boosted heterogeneous chemistry in the middle stratosphere and enhanced ozone production, compensating for up to 70% of the ozone depletion in the lower stratosphere. As global warming continues, we expect a growing frequency and importance of SCVs in promoting the impacts of wildfires on stratospheric aerosols and chemistry.

Characterization of winter PM2.5 source contributions and impacts of meteorological conditions and anthropogenic emission changes in the Sichuan Basin, 2002-2020

In this study, the Weather Research and Forecasting (WRF) model and Community Multiscale Air Quality-Integrated Source Apportionment Method (CMAQ-ISAM) were utilized, which were integrated with the Multiresolution Emission Inventory for China (MEIC) emission inventory, to simulate winter PM2.5 concentrations, regional transport, and changes in emission source contributions in the Sichuan basin (SCB) from 2002 to 2020, considering variations in meteorological conditions and anthropogenic emissions. The results indicated a gradual decrease in the basin's winter average PM2.5 concentration from 300 μg/m3 to 120 μg/m3, with the most significant decrease occurring after 2014, reflecting the actual impact of China's air pollution control measures. Spatially, the main pollution area shifted from Chongqing to Chengdu and the western basin. The sources of PM2.5 at the eastern and western margins of the basin have remained stable and have been dominated by local emissions for many years, while the sources of PM2.5 in the central part of the basin have evolved from a multiregional co-influenced source during the early period to a high proportion of local emissions; except for boundary condition sources, residential sources were the main PM2.5 sources in the basin (approximately 29.70 %), followed by industrial sources (approximately 14.11 %). Industrial sources exhibited higher contributions in Chengdu and Chongqing and gradually stabilized with residential sources over the years, while residential sources dominated in the eastern and western parts of the basin and exhibited a declining trend. Meteorological conditions exacerbated pollution in the whole basin from 2008 to 2014, especially in the west (21-40 μg/m3). The eastern basin and Chongqing exhibited more years with alleviated meteorological pollution, including a 40+ μg/m3 decrease in Chongqing from 2002 to 2005. Reduced anthropogenic emissions alleviated annual pollution levels, with a greater reduction (> -20 μg/m3) after 2011 due to pollution control measures.

Implications of ozone transport on air quality in the Sichuan Basin, China

Ozone pollution is formed through complex chemical and physical processes closely associated with emissions, photochemical reactions, and meteorological conditions. The objective of this study is to quantify the contributions of meteorological chemical formation, vertical transport, and horizontal transport to air quality during spring and summer in different regions of the Sichuan Basin. The Community Multi-scale Air Quality (CMAQ) with the Integrated Process Rate (IPR) was employed to simulate the months of April and July 2021 in the Sichuan Basin. The results indicate that both the spring and summer chemical formation of ozone in the urban centre show negative values, while the surrounding urban areas contribute positively, with chemical formation ranging from 0 to 10 μg·m-3. The maximum ozone level due to horizontal transport in the urban centre exceeds 20 μg·m-3, whereas horizontal transport in the surrounding urban areas exhibits negative values, with transport contributions concentrated within the range of -5 to 0 μg·m-3. The vertical transport in the central and southern parts of the basin shows positive values, with transport contributions ranging from 0 to 10 μg·m-3, and the urban centre exhibits relatively stronger vertical transport with contributions ranging from 10 to 20 μg·m-3. Although the chemical formation contribution in the urban centre is relatively small due to high nitrogen oxide emissions, vertical and horizontal transport play significant roles and are among the key factors contributing to ozone pollution formation.

Moderating AC Usage Can Reduce Thermal Disparity between Indoor and Outdoor Environments

In the context of escalating urban heat events due to climate change, air conditioning (AC) has become a critical factor in maintaining indoor thermal comfort. Yet the usage of AC can also exacerbate outdoor heat stress and burden the electricity system, and there is little scientific knowledge regarding how to balance these conflicting goals. To address this issue, we established a coupled modeling approach, integrating the Weather Research and Forecasting model with the building energy model (WRF_BEP + BEM), and designed multiple AC usage scenarios. We selected Chongqing, China's fourth-largest megacity, as our study area due to its significant socioeconomic importance, the severity of extreme heat events, and the uniqueness of its energy infrastructure. Our analysis reveals that AC systems can substantially reduce indoor temperatures by up to 18 °C; however, it also identifies substantial nighttime warming (2-2.5 °C) and a decline in thermal comfort. Particularly for high-density neighborhoods, when we increase 2 °C indoors, the outdoor temperature can be alleviated by up to 1 °C. Besides, despite the limited capacity to regulate peak electricity demand, we identified that reducing the spatial cooled fraction, increasing targeted indoor temperature by 2 °C, and implementing temporal AC schedules can effectively lower energy consumption in high-density neighborhoods, especially the reduction of spatial cooled fraction (up to 50%). Considering the substantial demand for cooling energy, it is imperative to carefully assess the adequacy and continuity of backup energy sources. The study underscores the urgency of reassessing energy resilience and advocates for addressing the thermal equity between indoor and outdoor environments, contributing to the development of a sustainable and just urban climate strategy in an era of intensifying heat events.

Intercomparison of flux-, gradient-, and variance-based optical turbulence (C n2) parameterizations

For free-space optical communication or ground-based optical astronomy, ample data of optical turbulence strength (C n2) are imperative but typically scarce. Turbulence conditions are strongly site dependent, so their accurate quantification requires in situ measurements or numerical weather simulations. If C n2 is not measured directly (e.g., with a scintillometer), C n2 parameterizations must be utilized to estimate it from meteorological observations or model output. Even though various parameterizations exist in the literature, their relative performance is unknown. We fill this knowledge gap by performing a systematic three-way comparison of a flux-, gradient-, and variance-based parameterization. Each parameterization is applied to both observed and simulated meteorological variables, and the resulting C n2 estimates are compared against observed C n2 from two scintillometers. The variance-based parameterization yields the overall best performance, and unlike other approaches, its application is not limited to the lowest part of the atmospheric boundary layer (i.e. the surface layer). We also show that C n2 estimated from the output of the Weather Research and Forecasting model aligns well with observations, highlighting the value of mesoscale models for optical turbulence modeling.

Hotspot driven air pollution during crop residue burning season in the Indo-Gangetic Plain, India

Intensive crop residue burning (CRB) in northern India triggers severe air pollution episodes over the Indo-Gangetic Plain (IGP) each year during October and November. We have quantified the contribution of hotspot districts (HSDs) and total CRB to poor air quality over the IGP. Initially, we investigated the spatiotemporal distribution of CRB fire within the domain and pinpointed five HSD in each Punjab and Haryana. Furthermore, we have simulated air quality and quantified the impact of CRB using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), incorporating recent anthropogenic emissions (EDGAR v5) and biomass burning emissions (FINN v2.4) inventories, along with MOZART-MOSAIC chemistry. The key finding is that HSDs contributed ∼80% and ∼50% of the total fire counts in Haryana and Punjab, respectively. The model effectively captured observed PM₂.₅ concentrations, with a normalized mean bias (NMB) below 0.2 and R-squared (R2) exceeding 0.65 at the majority of validation sites. However, some discrepancies were observed at a few sites in Delhi, Punjab, Haryana, and West Bengal. The National Capital Region experienced the highest PM₂.₅ concentrations, followed by Punjab, Haryana, Uttar Pradesh, Bihar, and West Bengal. Moreover, HSDs were responsible for about 70% of the total increase in CRB-induced PM₂.₅ in the western, central, and eastern cities, and around 50% in the northern cities. By eliminating CRB emissions across the domain, we could potentially save approximately 18,000 lives annually. Policymakers, scientists, and institutions can leverage the framework to address air pollution at national and global scales by targeting source-specific hotspots. This approach, coupled with appropriate technological and financial solutions, can contribute to achieving climate change and sustainable development goals.

High resolution Tibetan Plateau regional reanalysis 1961-present

With the rapid global warming in recent decades, the Tibetan Plateau (TP) has suffered severe impacts, such as glacier retreat, glacial lake expansion, and permafrost degradation, which threaten the lives and properties of the local and downstream populations. Regional Reanalysis (RR) is vital for TP due to the limitations of observations. In this work, a 62-year (1961-2022) long atmospheric regional reanalysis with spatial resolution of 9 km (convective gray-zone scale) and temporal resolution of 1 hour over the TP (TPRR) was developed using the Weather Research and Forecasting (WRF) model, combined with re-initialization method, spectral nudging (SN), and several optimizations. TPRR is forced by ERA5 at hourly intervals. TPRR outperforms ERA5, realistically capturing climatological characteristics and seasonal variations of precipitation and T2m (air temperature at 2m above ground level). Moreover, TPRR better reproduces the frequency and intensity of precipitation, as well as the diurnal cycle of precipitation. This study also quantifies the wetting trend of 0.0071 mm/year over the TP amid global warming using TPRR.

Assessing the role of cold front passage and synoptic patterns on air pollution in the Korean Peninsula

Various numerical experiments using WRF (Weather Research & Forecasting Model) and CMAQ (Community Multiscale Air Quality Modeling System) were performed to analyze the phenomenon of rapidly high concentration PM2.5 after the passage of a cold front in an area with limited local emissions. The episode period was from January 14 to 23, 2018, and analysis was conducted by dividing it into two stages according to the characteristics of changes in PM2.5 concentrations during the period. Through the analysis of observational data during the episode period, we confirmed meteorological impacts (decrease in temperature, increase in wind speed and relative humidity) and an increase in air pollution (PM10 and PM2.5) attributed to the passage of a cold front. Using CMAQ's IPR (Integrated Process Rate) analysis, the contribution of the horizontal advection process was observed in transporting PM2.5 to Gangneung at higher altitudes, and the PM2.5 concentrations at the surface increased because the vertical advection process was influenced by the terrain. Notably, in Stage 2 (64 μg·m-3), a higher contribution of the vertical advection process compared to Stage 1 (35 μg·m-3) was observed, which is attributed to the differences in synoptic patterns following the passage of the cold front. During Stage 2, following the cold front, atmospheric stability (dominance of high-pressure system) led to air subsidence and the presence of a temperature inversion layer, creating favorable meteorological conditions for the accumulation of air pollutants. This study offers the mechanisms of air pollution over the Korean Peninsula under non-stationary meteorological conditions, particularly in relation to the passage of the cold front (low-pressure system). Notably, the influence of a cold front can vary according to the synoptic patterns that develop following its passage.

Error Model for the Assimilation of All-Sky FY-4A/AGRI Infrared Radiance Observations

The Advanced Geostationary Radiation Imager (AGRI) carried by the FengYun-4A (FY-4A) satellite enables the continuous observation of local weather. However, FY-4A/AGRI infrared satellite observations are strongly influenced by clouds, which complicates their use in all-sky data assimilation. The presence of clouds leads to increased uncertainty, and the observation-minus-background (O-B) differences can significantly deviate from the Gaussian distribution assumed in the variational data assimilation theory. In this study, we introduce two cloud-affected (Ca) indices to quantify the impact of cloud amount and establish dynamic observation error models to address biases between O-B and Gaussian distributions when assimilating all-sky data from FY-4A/AGRI observations. For each Ca index, we evaluate two dynamic observation error models: a two-segment and a three-segment linear model. Our findings indicate that the three-segment linear model we propose better conforms to the statistical characteristics of FY-4A/AGRI observations and improves the Gaussianity of the O-B probability density function. Dynamic observation error models developed in this study are capable of handling cloud-free or cloud-affected FY-4A/AGRI observations in a uniform manner without cloud detection.

Triple dip La-Nina, unorthodox circulation and unusual spin in air quality of India

The recent La-Nina phase of the El Nino Southern Oscillation (ENSO) phenomenon unusually lasted for third consecutive year, has disturbed global weather and linked to Indian monsoon. However, our understanding on the linkages of such changes to regional air quality is poor. We hereby provide a mechanism that beyond just influencing the meteorology, the interactions between the ocean and the atmosphere during the retreating phase of the La-Niña produced secondary results that significantly influenced the normal distribution of air quality over India through disturbed large-scale wind patterns. The winter of 2022-23 that coincided with retreating phase of the unprecedented triple dip La-Niña, was marred by a mysterious trend in air quality in different climatological regions of India, not observed in recent decades. The unusually worst air quality over South-Western India, whereas relatively cleaner air over the highly polluted North India, where levels of most toxic pollutant (PM2.5) deviating up to about ±30 % from earlier years. The dominance of higher northerly wind in the transport level forces influx and relatively slower winds near the surface, trapping pollutants in peninsular India, thereby notably increasing PM2.5 concentration. In contrast, too feeble western disturbances, and unique wind patterns with the absence of rain and clouds and faster ventilation led to a significant improvement in air quality in the North. The observed findings are validated by the chemical-transport model when forced with the climatology of the previous year. The novelty of present research is that it provides an association of air quality with climate change. We demonstrate that the modulated large-scale wind patterns linked to climatic changes may have far-reaching consequences even at a local scale leading to unusual changes in the distribution of air pollutants, suggesting ever-stringent emission control actions.

California Case Study of Wildfires and Prescribed Burns: PM2.5 Emissions, Concentrations, and Implications for Human Health

Wildfires are a significant threat to human health, in part through degraded air quality. Prescribed burning can reduce wildfire severity but can also lead to an increase in air pollution. The complexities of fires and atmospheric processes lead to uncertainties when predicting the air quality impacts of fire and make it difficult to fully assess the costs and benefits of an expansion of prescribed fire. By modeling differences in emissions, surface conditions, and meteorology between wildfire and prescribed burns, we present a novel comparison of the air quality impacts of these fire types under specific scenarios. One wildfire and two prescribed burn scenarios were considered, with one prescribed burn scenario optimized for potential smoke exposure. We found that PM2.5 emissions were reduced by 52%, from 0.27 to 0.14 Tg, when fires burned under prescribed burn conditions, considerably reducing PM2.5 concentrations. Excess short-term mortality from PM2.5 exposure was 40 deaths for fires under wildfire conditions and 39 and 15 deaths for fires under the default and optimized prescribed burn scenarios, respectively. Our findings suggest prescribed burns, particularly when planned during conditions that minimize smoke exposure, could be a net benefit for the impacts of wildfires on air quality and health.

Impacts of emissions and meteorological conditions in three different phases of aerosol pollution during 2013-2022 in Anhui, China

The PM2.5 concentrations in Anhui, which links the Yangtze River Delta region, China's fastest growing economy area, with the Beijing-Tianjin-Hebei (BTH) region, China's most polluted region, are influenced not only by emissions, but also by variation of meteorological conditions. A comprehensive understanding of the relative impacts of meteorology and emissions on heavy pollution in Anhui at three phases (i.e., phase1: from 2013 to 2017; phase2: from 2018 to 2020; phase 3: from 2021 to 2022) from 2013 to 2022, which can provide suggestions for pollution prevention and control in the future. The decrease in pollutant concentrations from 2013 to 2022 is mainly attributed to the continued reduction in emissions, while the year-to-year fluctuations in pollutant concentrations are largely influenced by meteorological conditions. Although emissions are decreasing, the proportions of residential biofuel combustion and cement are increasing. In addition to the effects of prevailing northeasterly and northwesterly winds (i.e., Type1 and Type2), there is also concern about the influences of static weather and neighboring regional transport (i.e., Type5 and Type6), especially in 2016. The contribution of emissions is greater in phase 2 and phase 3, with a 17 % increase compared to phase 1. Overall, approximately 57 % of explosive growth in PM2.5 concentration during the cumulative stage (CS) can be regarded as the feedback effect of the deteriorating meteorological conditions. Therefore, statistical analyses show that limiting PM2.5 concentrations below about 73 μg m-3 would weaken the feedback effects, which in turn would avoid most of the explosive growth processes in the CS of the 60 heavy pollution processes, which can provide a reference for the government to set a target for sustained emission reduction.

Impacts of anthropogenic emissions and meteorology on spring ozone differences in San Antonio, Texas between 2017 and 2021

San Antonio has been designated as ozone nonattainment under the current National Ambient Air Quality Standards (NAAQS). Ozone events in the city typically occur in two peaks, characterized by a pronounced spring peak followed by a late summer peak. Despite higher ozone levels, the spring peak has received less attention than the summer peak. To address this research gap, we used the Weather Research and Forecasting (WRF)-driven GEOS-Chem (WRF-GC) model to simulate San Antonio's ozone changes in the spring month of May from 2017 to 2021 and quantified the respective contributions from changes in anthropogenic emissions and meteorology. In addition to modeling, observations from the San Antonio Field Studies (SAFS), the Texas Commission on Environmental Quality (TCEQ) Continuous Ambient Monitoring Stations (CAMS), and the spaceborne TROPOspheric Monitoring Instrument (TROPOMI) are used to examine and validate changes in ozone and precursors. Results show that the simulated daytime mean surface ozone in May 2021 is 3.8 ± 0.6 ppbv lower than in May 2017, which is slightly less than the observed average differences of -5.3 ppbv at CAMS sites. The model predicted that the anthropogenic emission-induced changes contribute to a 1.4 ± 0.5 ppbv reduction in daytime ozone levels, while the meteorology-induced changes account for a 2.4 ± 0.6 ppbv reduction over 2017-2021. This suggests that meteorology plays a relatively more important role than anthropogenic emissions in explaining the spring ozone differences between the two years. We additionally identified (1) reduced NO2 and HCHO concentrations as chemical reasons, and (2) lower temperature, higher humidity, increased wind speed, and a stronger Bermuda High as meteorological reasons for lower ozone levels in 2021 compared to 2017. The quantification of the different roles of meteorology and ozone precursor concentrations helps understand the cause and variation of ozone changes in San Antonio over recent years.

A cautious note advocating the use of ensembles of models and driving data in modeling of regional ozone burdens

We investigate the performance of two widely used chemistry-transport models (CTMs) with different chemical mechanisms in reproducing the ambient maximum daily 8-h average ozone (MDA8 O3) burden over Central Europe. We explore a base case setup with boundary conditions (BC) for meteorology from the ERA-Interim reanalysis and chemical BC from CAM-Chem as well as effects of alterations in these BC based on global model fields. Our results show that changes in meteorological BC strongly affect the correlation with observations but only marginally affect the model biases, while changes in chemical BC increase model biases while correlation patterns remain largely unchanged. Furthermore, our study highlights that CTM choice (and choice of chemical mechanism) has a similar or even larger impact on MDA8 O3 levels as the impact of altered BC. In summary, our study calls for a multi-model strategy combining different CTM and BC combinations to explore the bandwidth of MDA8 O3 distributions and thus uncertainty in hindcasts and future projections, in analogy to climate studies considering ensemble simulations under the same anthropogenic emissions but with slightly different initial conditions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11869-024-01516-3.

Analyzing urban influence on extreme winter precipitation through observations and numerical simulation of two South China case studies

This study investigates the impact of urbanization on extreme winter rainfall in the South China Greater Bay Area (GBA) through the analysis of hourly station observations and simulations using the Weather Research and Forecasting Model with the Single Layer Urban Canopy Model (WRF-SLUCM). Data from 2008 to 2017 reveal that urban areas in the GBA experience lower 99th percentile hourly winter rainfall intensity compared to surrounding rural regions. However, urban locations exhibit higher annual maximum hourly rainfall (Rmax) and very extreme rainfall events (99.99th percentile) in winter, suggesting a positive influence of urbanization on extreme winter precipitation. A case study further underscores the role of the Urban Heat Island (UHI) effect in enhancing extreme rainfall intensity and probability in the GBA urban areas. Additionally, two extreme cases were dynamically downscaled using WRF-SLUCM, involving four parallel experiments: replacing urban land use with cropland (Nourban), using historical urban land use data from 1999 (99LS), projecting near-future urban land use for 2030 (30LS), and considering 2030 urban land use without anthropogenic heat (AH) (30LS-AH0). Synoptic analysis demonstrates that cold air intrusion suppresses the GBA UHI in Case 2013 but not in Case 2015. Reduced evaporation and humidity induced by urban surfaces significantly decrease urban precipitation in Case 2013. In contrast, the persistent UHI in Case 2015 enhances local convection and land-ocean circulation, leading to increased moisture flux convergence and amplified urban precipitation intensity and probability in 30LS compared to Nourban. This amplification is primarily attributed to AH, while the change in 99LS remains insignificant. These findings suggest that urban influences on extreme precipitation in the GBA persist during winter, particularly when the UHI effect is maintained.

Evaluation of precipitation across the contiguous United States, Alaska, and Puerto Rico in multi-decadal convection-permitting simulations

This study is an early effort to generate a multi-decadal convection-permitting regional climate dataset that covers nearly the entire North American continent. We assessed a 20 year dynamically downscaled regional climate simulation at a 4 km spatial resolution with explicit convection across the contiguous United States (CONUS), Alaska, and Puerto Rico. Specifically, we evaluated the model's performance in representing mean, 95th percentile, and extreme precipitation across regions. Our findings indicate that when compared with ERA5 reanalysis, the forcing data, convection-permitting simulation improves representations of seasonal, 95th percentile, and extreme precipitation over a large portion of the CONUS, Alaska, and Puerto Rico, particularly in areas where precipitation is heaviest. The simulation adds value over its forcing data (ERA5) in up to 53% of all grid cells in the CONUS, 68.8% in Alaska, and 84.0% in Puerto Rico. It is important to note that, however, despite improvements, model errors in Puerto Rico remain large. Similar improvements are observed in extreme indices, including consecutive dry days, maximum 5 days precipitation, and extreme precipitation. Analysis of the diurnal cycle of mean hourly precipitation suggests that representations of convective processes-including onset, dissipation, suppression, downstream propagation, and local circulation-improved overall.

WRF-Chem modeling study of heat wave driven ozone over southeast region, India

Present study examines how ozone concentration changed under heatwave (HW) condition with emphasis on meteorological parameters in respect to non-heatwave (NHW) days. In this perspective, Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) has been used to simulate the surface O3 (SfO3) and maximum temperature (Tmax) during NHW (11th-19th May 2015) and HW days (21st-29th May 2015) over southeast (SE), India. The WRF-Chem simulated meteorological and chemical variables have been evaluated against the ERA5 and CAMS reanalysis dataset. A significant correlation of 55-95% is found for all the meteorological and chemical variables. The influencing parameters shows positive correlation of ozone with temperature, which reaches 75-78 ppbv under HW condition. Day to day trend analysis reveal an increasing pattern of maximum temperature and SfO3 concentration under HW condition. During HW, mixing of ozone-rich air aloft with near-surface air leading a rise in SfO3, as indicated by both ERA5 (with a maximum Planetary Boundary Layer Height (PBLH) of 1000 m) and WRF-Chem simulations (1600 m). Furthermore, the diurnal cycle of SfO3, temperature, PBLH reaches a peak at afternoon, while the other variables like nitrogen oxides (NOx), Relative Humidity (RH) shows a high concentration at night-time. Overall, WRF-Chem model effectively captures the diurnal fluctuations of SfO3, NOx and the meteorological variables during the HW event over the SE, India. Result shows that HW may cause a strong contribution to the rate of increase in SfO3 (22.17%). Thus, it is required to consider contribution of HW driven ozone when developing long-term strategies to mitigate regional ozone pollution.

Air pollution analysis in Northwestern South America: A new Lagrangian framework

This study examines the spatiotemporal variations of PM2.5, PM10, SO2, O3, NO, and NO2 concentrations in Northwestern South America (NWSA). We assess the efficacy of existing policies, identify underlying phenomena, and highlight areas for further research. Significant findings have emerged by analyzing reanalysis and in-situ data, employing the WRF-Chem model, and utilizing a new Lagrangian framework designed to overcome some drawbacks common to analysis of pollution Long-Range Transport. Wildfires in the first half of the year and volcanic activity (for SO2) in July-August contribute to over 90 % of the pollutant's advection, leading to high pollution levels in urban areas. SO2 volcanic emissions contribute to secondary PM, explaining the peak in PM concentrations in Cali in July. In the second half of the year, pollutant behavior varies based on factors such as city characteristics, vehicular-volume, air temperature, wind speed, and boundary layer height, and O3 is influenced by solar radiation and the NO/NO2 ratio. Diurnal variations of PM and NOx correlate with vehicular density, SO2 with industrial activity, and O3 depends on solar radiation. Trend analysis reveals decreasing PM10 levels except in three Cundinamarca cities and Cali suggesting the need to implement/evaluate control plans in those locations. Although data is limited, NO and NO2 levels show an increasing trend due to the rising number of vehicles. SO2 levels are decreasing, except in Cali, potentially influenced by the nearby industrial and polluted city of Yumbo. O3 displays a downward trend in most cities, except Bogotá, due to the NO/NO2 ratio favoring O3 increase. These findings provide a starting point for further research to deepen our understanding of NWSA air pollution. Such investigations are essential before modifying existing policies or enacting new ones. Collaborative efforts at the international, regional, and inter-city levels are crucial for effective air quality management.

Positive feedback to regional climate enhances African wildfires

Regional climate strongly regulates the occurrence of wildfires partly because drying of fuel load increases fires. The large amounts of aerosols released by wildfires can also strongly affect regional climate. Here we show positive feedback (a seasonal burned area enhancement of 7-17%) due to wildfire aerosol forcing in Africa found in the simulations using the interactive REgion-Specific ecosystem feedback Fire (RESFire) model in the Community Earth System Model (CESM). The positive feedback results partly from the transport of fire aerosols from burning (dry) to wet regions, reducing precipitation and drying fuel load to enhance fires toward the non-burning (wet) region. This internally self-enhanced burning is an important mechanism for the regulation of African ecosystems and for understanding African fire behaviors in a changing climate. A similar mechanism may also help sustain wildfires in other tropical regions.

Wintertime Formation of Large Sulfate Particles in China and Implications for Human Health

Outdoor air pollution causes millions of premature deaths annually worldwide. Sulfate is a major component of particulate pollution. Winter sulfate observations in China show both high concentrations and an accumulation mode with a modal size >1 μm. However, we find that this observed size distribution cannot be simulated using classical gaseous and aqueous phase formation (CSF) or proposed aerosol-processing formation (APF) mechanisms. Specifically, the CSF simulation underestimates sulfate concentrations by 76% over megacities in China and predicts particle size distributions with a modal size of ∼0.35 μm, significantly smaller than observations. Although incorporating the APF mechanism in the atmospheric chemical model notably improves sulfate concentration simulation with reasonable parameters, the simulated sulfate particle size distribution remains similar to that using the CSF mechanism. We further conduct theoretical analyses and show that particles with diameters <0.3 μm grow rapidly (2-3 s) to 1 μm through the condensation of sulfuric acid in fresh high-temperature exhaust plumes, referred to as in-source formation (ISF). An ISF sulfate source equivalent to 15% of sulfur emissions from fossil fuel combustion largely explains both observed size distributions and mass concentrations of sulfate particles. The findings imply that ISF is a major source of wintertime micron-sized sulfate in China and underscore the importance of considering the size distribution of aerosols for accurately assessing the impacts of inorganic aerosols on radiative forcing and human health.

Increasing Nonfossil Fuel Contributions to Atmospheric Nitrate in Urban China from Observation to Prediction

China's nitrogen oxide (NOx) emissions have undergone significant changes over the past few decades. However, nonfossil fuel NOx emissions are not yet well constrained in urban environments, resulting in a substantial underestimation of their importance relative to the known fossil fuel NOx emissions. We developed an approach using machine learning that is accurate enough to generate a long time series of the nitrogen isotopic composition (δ15N) of atmospheric nitrate using high-level accuracies of air pollutants and meteorology data. Air temperature was found to be the critical driver of the variation of nitrate δ15N at daily resolution based on this approach, while significant reductions of aerosol and its precursor emissions played a key role in the change of nitrate δ15N on the yearly scale. Predictions from this model found a significant decrease in nitrate δ15N in Chinese megacities (Beijing and Guangzhou as representative cities in the north and south, respectively) since 2013, implying an enhanced contribution of nonfossil fuel NOx emissions to nitrate aerosols (up to 22%-26% in 2021 from 18%-22% in 2013 quantified by an isotope mixing model), as confirmed by the Weather Research and Forecasting model coupled with online chemistry (WRF-Chem) simulation. Meanwhile, the declining contribution in coal combustion (34%-39% in 2013 to 31%-34% in 2021) and increasing contribution of natural gas combustion (11%-14% in 2013 to 14%-17% in 2021) demonstrated the transformation of China's energy structure from coal to natural gas. This approach provides missing records for exploring long-term variability in the nitrogen isotope system and may contribute to the study of the global reactive nitrogen biogeochemical cycle.

The Performance of GRAMM-SCI and WRF in Simulating the Surface-Energy Budget and Thermally Driven Winds in an Alpine Valley

Using WRF as a benchmark, GRAMM-SCI simulations are performed for a case study of thermally driven valley- and slope winds in the Inn Valley, Austria. A clear-sky, synoptically undisturbed day was selected when large spatial heterogeneities occur in the components of the surface-energy budget driven by local terrain and land-use characteristics. The models are evaluated mainly against observations from four eddy-covariance stations in the valley. While both models are able to capture the main characteristics of the surface-energy budget and the locally driven wind field, a few overall deficiencies are identified: (i) Since the surface-energy budget is closed in the models, whereas large residuals are observed, the models generally tend to overestimate the daytime sensible and latent heat fluxes. (ii) The partitioning of the available energy into sensible and latent heat fluxes remains relatively constant in the simulations, whereas the observed Bowen ratio decreases continuously throughout the day because of a temporal shift between the maxima in sensible and latent heat fluxes, which is not captured by the models. (iii) The comparison between model results and observations is hampered by differences between the real land use and the vegetation type in the model. Recent modifications of the land-surface scheme in GRAMM-SCI improve the representation of nighttime katabatic winds over forested areas, reducing the modeled wind speeds to more realistic values.

Impacts of Ship Emissions on Air Quality in Southern China: Opportunistic Insights from the Abrupt Emission Changes in Early 2020

In early 2020, two unique events perturbed ship emissions of pollutants around Southern China, proffering insights into the impacts of ship emissions on regional air quality: the decline of ship activities due to COVID-19 and the global enforcement of low-sulfur (<0.5%) fuel oil for ships. In January and February 2020, estimated ship emissions of NOx, SO2, and primary PM2.5 over Southern China dropped by 19, 71, and 58%, respectively, relative to the same period in 2019. The decline of ship NOx emissions was mostly over the coastal waters and inland waterways of Southern China due to reduced ship activities. The decline of ship SO2 and primary PM2.5 emissions was most pronounced outside the Chinese Domestic Emission Control Area due to the switch to low-sulfur fuel oil there. Ship emission reductions in early 2020 drove 16 to 18% decreases in surface NO2 levels but 3.8 to 4.9% increases in surface ozone over Southern China. We estimated that ship emissions contributed 40% of surface NO2 concentrations over Guangdong in winter. Our results indicated that future abatements of ship emissions should be implemented synergistically with reductions of land-borne anthropogenic emissions of nonmethane volatile organic compounds to effectively alleviate regional ozone pollution.

Urban Versus Lake Impacts on Heat Stress and Its Disparities in a Shoreline City

Shoreline cities are influenced by both urban-scale processes and land-water interactions, with consequences on heat exposure and its disparities. Heat exposure studies over these cities have focused on air and skin temperature, even though moisture advection from water bodies can also modulate heat stress. Here, using an ensemble of model simulations covering Chicago, we find that Lake Michigan strongly reduces heat exposure (2.75°C reduction in maximum average air temperature in Chicago) and heat stress (maximum average wet bulb globe temperature reduced by 0.86°C) during the day, while urbanization enhances them at night (2.75 and 1.57°C increases in minimum average air and wet bulb globe temperature, respectively). We also demonstrate that urban and lake impacts on temperature (particularly skin temperature), including their extremes, and lake-to-land gradients, are stronger than the corresponding impacts on heat stress, partly due to humidity-related feedback. Likewise, environmental disparities across community areas in Chicago seen for skin temperature are much higher (1.29°C increase for maximum average values per $10,000 higher median income per capita) than disparities in air temperature (0.50°C increase) and wet bulb globe temperature (0.23°C increase). The results call for consistent use of physiologically relevant heat exposure metrics to accurately capture the public health implications of urbanization.

Uncertainty in surface wind speed projections over the Iberian Peninsula: CMIP6 GCMs versus a WRF-RCM

This study assessed the projected near-surface wind speed (SWS) changes and variability over the Iberian Peninsula for the 21st century. Here, we compared Coupled Model Intercomparison Project Phase 6 global climate models (GCMs) with a higher spatial resolution regional climate model (RCM; ∼20 km), known as WRF-CESM2, which was created by a dynamic downscaling of the Community Earth System Model version 2 (CESM2) using the Weather Research and Forecasting (WRF) model. Our analysis found that the GCMs tended to overestimate observed SWS for 1985-2014, while the higher spatial resolution of the WRF-CESM2 did not improve the accuracy and underestimated the SWS magnitude. GCMs project a decline of SWS under high shared socioeconomic pathways (SSPs) greenhouse concentrations, such as SSP370 and SSP585, while an interdecadal oscillation appears in SSP126 and SSP245 for the end of the century. The WRF-CESM2 under SSP585 predicts the opposite increasing SWS. Our results suggest that 21st-century projections of SWS are uncertain even for regionalized products and should be taken with caution.

Continental United States climate projections based on thermodynamic modification of historical weather

Regional climate models can be used to examine how past weather events might unfold under different climate conditions by simulating analogue versions of those events with modified thermodynamic conditions (i.e., warming signals). Here, we apply this approach by dynamically downscaling a 40-year sequence of past weather from 1980-2019 driven by atmospheric re-analysis, and then repeating this 40-year sequence a total of 8 times using a range of time-evolving thermodynamic warming signals that follow 4 80-year future warming trajectories from 2020-2099. Warming signals follow two emission scenarios (SSP585 and SSP245) and are derived from two groups of global climate models based on whether they exhibit relatively high or low climate sensitivity. The resulting dataset, which contains 25 hourly and over 200 3-hourly variables at 12 km spatial resolution, can be used to examine a plausible range of future climate conditions in direct reference to previously observed weather and enables a systematic exploration of the ways in which thermodynamic change influences the characteristics of historical extreme events.

Global Health and Climate Effects of Organic Aerosols from Different Sources

The impact of aerosols on human health and climate is well-recognized, yet many studies have only focused on total PM2.5 or changes from anthropogenic activities. This study quantifies the health and climate effects of organic aerosols (OA) from anthropogenic, biomass burning, and biogenic sources. Using two atmospheric chemistry models, CAM-chem and GEOS-Chem, our findings reveal that anthropogenic primary OA (POA) has the highest efficiency for health effects but the lowest for direct radiative effects due to spatial and temporal variations associated with population and surface albedo. The treatment of POA as nonvolatile or semivolatile also influences these efficiencies through different chemical processes. Biogenic OA shows moderate efficiency for health effects and the highest for direct radiative effects but has the lowest efficiency for indirect effects due to the reduced high cloud, caused by stabilized temperature profiles from aerosol-radiation interactions in biogenic OA-rich regions. Biomass burning OA is important for cloud radiative effect changes in remote atmospheres due to its ability to be transported further than other OAs. This study highlights the importance of not only OA characteristics such as toxicity and refractive index but also atmospheric processes such as transport and chemistry in determining health and climate impact efficiencies.

Bioaerosols are the dominant source of warm-temperature immersion-mode INPs and drive uncertainties in INP predictability

Ice-nucleating particles (INPs) are rare atmospheric aerosols that initiate primary ice formation, but accurately simulating their concentrations and variability in large-scale climate models remains a challenge. Doing so requires both simulating major particle sources and parameterizing their ice nucleation (IN) efficiency. Validating and improving model predictions of INP concentrations requires measuring their concentrations delineated by particle type. We present a method to speciate INP concentrations into contributions from dust, sea spray aerosol (SSA), and bioaerosol. Field campaign data from Bodega Bay, California, showed that bioaerosols were the primary source of INPs between -12° and -20°C, while dust was a minor source and SSA had little impact. We found that recent parameterizations for dust and SSA accurately predicted ambient INP concentrations. However, the model did not skillfully simulate bioaerosol INPs, suggesting a need for further research to identify major factors controlling their emissions and INP efficiency for improved representation in models.

Influence of the freezing level on atmospheric rivers in High Mountain Asia: WRF case studies of orographic precipitation extremes

Atmospheric rivers (ARs) reach High Mountain Asia (HMA) about 10 days per month during the winter and spring, resulting in about 20 mm day-1 of precipitation. However, a few events may exceed 100 mm day-1, providing most of the total winter precipitation and increasing the risk of precipitation-triggered landslides and flooding, particularly when the height of the height of the 0 ∘C isotherm, or freezing level is above-average. This study shows that from 1979 to 2015, integrated water vapor transport (IVT) during ARs that reach Western HMA has increased 16% while the freezing level has increased up to 35 m. HMA ARs that have an above-average freezing level result in 10-40% less frozen precipitation compared to ARs with a below-average freezing level. To evaluate the importance of these trends in the characteristics of ARs, we investigate mesoscale processes leading to orographic precipitation using Advanced Weather Research and Forecasting (ARW-WRF) simulations at 6.7 km spatial resolution. We contrast two above- and below- average freezing level AR events with otherwise broadly similar characteristics and show that with a 50-600 m increase in freezing level, the above-average AR resulted in 10-70% less frozen precipitation than the below-average event. This study contributes to a better understanding of climate change-related impacts within HMA's hydrological cycle and the associated hazards to vulnerable communities living in the region.

Modeling Impacts of Urbanization on Winter Boundary Layer Meteorology and Aerosol Pollution in the Central Liaoning City Cluster, China

The influence of urbanization on the frequent winter aerosol pollution events in Northeast China is not fully understood. The Weather Research and Forecasting Model with Chemistry (WRF-Chem) coupled with urban canopy (UC) models was used to simulate the impact of urbanization on an aerosol pollution process in the Central Liaoning city cluster (CLCC), China. To investigate the main mechanisms of urban expansion and UC on the winter atmospheric environment and the atmospheric diffusion capacity (ADC) in the CLCC, three simulation cases were designed using land-use datasets from different periods and different UC schemes. A comparative analysis of the simulation results showed that the land-use change (LU) and both LU and UC (LUUC) effects lead to higher surface temperature and lower relative humidity and wind speed in the CLCC by decreasing surface albedo, increasing sensible heat flux, and increasing surface roughness, with a spatial distribution similar to the distribution of LU. The thermal effect leads to an increase in atmospheric instability, an increase in boundary layer height and diffusion coefficient, and an increase in the ADC. The LU and LUUC effects lead to a significant decrease in near-surface PM2.5 concentrations in the CLCC due to changes in meteorological conditions and ADC within the boundary layer. The reduction in surface PM2.5 concentrations due to the LU effect is stronger at night than during daytime, while the LUUC effect leads to a greater reduction in surface PM2.5 concentrations during the day, mainly due to stronger diffusion and dilution caused by the effect of urban turbulence within different levels caused by the more complex UC scheme. In this study, the LU and LUUC effects result in greater thermal than dynamic effects, and both have a negative impact on surface PM2.5 concentrations, but redistribute pollutants from the lower urban troposphere to higher altitudes.

An Analysis of CMAQ Gas Phase Dry Deposition over North America Through Grid-Scale and Land-Use Specific Diagnostics in the Context of AQMEII4

The fourth phase of the Air Quality Model Evaluation International Initiative (AQMEII4) is conducting a diagnostic intercomparison and evaluation of deposition simulated by regional-scale air quality models over North America and Europe. In this study, we analyze annual AQMEII4 simulations performed with the Community Multiscale Air Quality Model (CMAQ) version 5.3.1 over North America. These simulations were configured with both the M3Dry and Surface Tiled Aerosol and Gas Exchange (STAGE) dry deposition schemes available in CMAQ. A comparison of observed and modeled concentrations and wet deposition fluxes shows that the AQMEII4 CMAQ simulations perform similarly to other contemporary regional-scale modeling studies. During summer, M3Dry has higher ozone (O3) deposition velocities (Vd) and lower mixing ratios than STAGE for much of the eastern U.S. while the reverse is the case over eastern Canada and along the West Coast. In contrast, during winter STAGE has higher O3 Vd and lower mixing ratios than M3Dry over most of the southern half of the modeling domain while the reverse is the case for much of the northern U.S. and southern Canada. Analysis of the diagnostic variables defined for the AQMEII4 project, i.e. grid-scale and land-use (LU) specific effective conductances and deposition fluxes for the major dry deposition pathways, reveals generally higher summertime stomatal and wintertime cuticular grid-scale effective conductances for M3Dry and generally higher soil grid-scale effective conductances (for both vegetated and bare soil) for STAGE in both summer and winter. On a domain-wide basis, the stomatal grid-scale effective conductances account for about half of the total O3 Vd during daytime hours in summer for both schemes. Employing LU-specific diagnostics, results show that daytime Vd varies by a factor of 2 between LU categories. Furthermore, M3Dry vs. STAGE differences are most pronounced for the stomatal and vegetated soil pathway for the forest LU categories, with M3Dry estimating larger effective conductances for the stomatal pathway and STAGE estimating larger effective conductances for the vegetated soil pathway for these LU categories. Annual domain total O3 deposition fluxes differ only slightly between M3Dry (74.4 Tg/year) and STAGE (76.2 Tg/yr), but pathway-specific fluxes to individual LU types can vary more substantially on both annual and seasonal scales which would affect estimates of O3 damages to sensitive vegetation. A comparison of two simulations differing only in their LU classification scheme shows that the differences in LU cause seasonal mean O3 mixing ratio differences on the order of 1 ppb across large portions of the domain, with the differences generally largest during summer and in areas characterized by the largest differences in the fractional coverages of the forest, planted/cultivated, and grassland LU categories. These differences are generally smaller than the M3Dry vs. STAGE differences outside the summer season but have a similar magnitude during summer. Results indicate that the deposition impacts of LU differences are caused both by differences in the fractional coverages and spatial distributions of different LU categories as well as the characterization of these categories through variables like surface roughness and vegetation fraction in look-up tables used in the land-surface model and deposition schemes. Overall, the analyses and results presented in this study illustrate how the diagnostic grid-scale and LU-specific dry deposition variables adopted for AQMEII4 can provide insights into similarities and differences between the CMAQ M3Dry and STAGE dry deposition schemes that affect simulated pollutant budgets and ecosystem impacts from atmospheric pollution.

Optimal reactive nitrogen control pathways identified for cost-effective PM2.5 mitigation in Europe

Excess reactive nitrogen (Nr), including nitrogen oxides (NO_x) and ammonia (NH₃), contributes strongly to fine particulate matter (PM_2.5) air pollution in Europe, posing challenges to public health. Designing cost-effective Nr control roadmaps for PM_2.5 mitigation requires considering both mitigation efficiencies and implementation costs. Here we identify optimal Nr control pathways for Europe by integrating emission estimations, air quality modeling, exposure-mortality modeling, Nr control experiments and cost data. We find that phasing out Nr emissions would reduce PM_2.5 by 2.3 ± 1.2 μg·m^-3 in Europe, helping many locations achieve the World Health Organization (WHO) guidelines and reducing PM_2.5-related premature deaths by almost 100 thousand in 2015. Low-ambition NH₃ controls have similar PM_2.5 mitigation efficiencies as NO_x in Eastern Europe, but are less effective in Western Europe until reductions exceed 40%. The efficiency for NH₃ controls increases at high-ambition reductions while NO_x slightly decreases. When costs are considered, strategies for both regions uniformly shift in favor of NH₃ controls, as NH₃ controls up to 50% remain 5-11 times more cost-effective than NO_x per unit PM_2.5 reduction, emphasizing the priority of NH₃ control policies for Europe.

The turbulent future brings a breath of fresh air

Ventilation of health hazardous aerosol pollution within the planetary boundary layer (PBL) - the lowest layer of the atmosphere - is dependent upon turbulent mixing, which again is closely linked to the height of the PBL. Here we show that emissions of both CO2 and absorbing aerosols such as black carbon influence the number of severe air pollution episodes through impacts on turbulence and PBL height. While absorbing aerosols cause increased boundary layer stability and reduced turbulence through atmospheric heating, CO2 has the opposite effect over land through surface warming. In future scenarios with increasing CO2 concentrations and reduced aerosol emissions, we find that around 10% of the world's population currently living in regions with high pollution levels are likely to experience a particularly strong increase in turbulence and PBL height, and thus a reduction in intense pollution events. Our results highlight how these boundary layer processes provide an added positive impact of black carbon mitigation to human health.

Natural short-lived halogens exert an indirect cooling effect on climate

Observational evidence shows the ubiquitous presence of ocean-emitted short-lived halogens in the global atmosphere1-3. Natural emissions of these chemical compounds have been anthropogenically amplified since pre-industrial times4-6, while, in addition, anthropogenic short-lived halocarbons are currently being emitted to the atmosphere7,8. Despite their widespread distribution in the atmosphere, the combined impact of these species on Earth's radiative balance remains unknown. Here we show that short-lived halogens exert a substantial indirect cooling effect at present (-0.13 ± 0.03 watts per square metre) that arises from halogen-mediated radiative perturbations of ozone (-0.24 ± 0.02 watts per square metre), compensated by those from methane (+0.09 ± 0.01 watts per square metre), aerosols (+0.03 ± 0.01 watts per square metre) and stratospheric water vapour (+0.011 ± 0.001 watts per square metre). Importantly, this substantial cooling effect has increased since 1750 by -0.05 ± 0.03 watts per square metre (61 per cent), driven by the anthropogenic amplification of natural halogen emissions, and is projected to change further (18-31 per cent by 2100) depending on climate warming projections and socioeconomic development. We conclude that the indirect radiative effect due to short-lived halogens should now be incorporated into climate models to provide a more realistic natural baseline of Earth's climate system.

Substantial short- and long-term health effect due to PM2.5 and the constituents even under future emission reductions in China

Heavy pollution events of fine particulate matter (PM_2.5) frequently occur in China, seriously affecting the human health. However, how meteorological factors and anthropogenic emissions affect PM_2.5 and the major constituents, as well as the subsequent health effect, remains unclear. Here, based on regional climate and air quality models Weather Research and Forecasting (WRF) and Community Multiscale Air Quality (CMAQ), the PM_2.5 and major constituents in China at present and mid-century under the carbon neutral scenario Shared Socioeconomic Pathways (SSP)1-2.6 are simulated. Due to anthropogenic emission reduction, concentrations of PM_2.5 and the constituents decrease substantially in SSP1-2.6. The long-term exposure premature deaths at present are 2.23 million per year in mainland China, which is projected to increase by 76 % under SSP1-2.6 despite emission reduction, primarily attributable to aging which strikingly offsets the effect of air quality improvement. The number of annual premature deaths resulting from short-term exposure is 228,104 in mainland China at present, which is projected to decrease in the future. Using North China Plain as an example, we identify that among the major constituents of PM_2.5, organic carbon leads to the most short-term exposure deaths considering the largest exposure-response coefficient. Regarding the abnormally meteorological conditions, we find, relative to low relative humidity (RH) and non-stagnation, the compound events, defined as concurrence of high RH and atmospheric stagnation, exhibit an amplified role inducing larger premature deaths compared to the additive effect of the individual event of high RH and atmospheric stagnation. This nonlinear effect occurs at both present and future, but diminished in future due to emission reductions. Our study highlights the importance of considering both the long- and short-term premature deaths associated with PM_2.5 and the constituents, as well as the critical effect of extreme weather events.

The underlying mechanisms of PM2.5 and O3 synergistic pollution in East China: Photochemical and heterogeneous interactions

The rapid development of Chinese cities is accompanied by air pollution. Although the implementation of air pollution control strategies in recent years has alleviated PM_2.5 pollution, O₃ pollution and the synergistic pollution of PM_2.5 and O₃ have become more serious. To understand the underlying chemical interaction mechanisms between PM_2.5 and O₃, we applied the modified Weather Research and Forecasting model with Chemistry (WRF-Chem) to study the effects of aerosol-photolysis feedback and heterogeneous reactions on the two pollutants and revealed the contribution of different mechanisms in different seasons and regions in Yangtze River Delta (YRD) in eastern China. We found that, through the aerosol-photolysis feedback, PM_2.5 decreased the surface photolysis rates J_NO2 and J_O1D, resulting in a decrease in O₃ concentration in the VOC-sensitive area and a slight increase in the NO_x-sensitive area. The heterogeneous reactions reduced O₃ concentration in the YRD in spring, autumn and winter by consuming H_xO_y. While in summer, the heterogeneous absorption of NO_x decreased O₃ in the NO_x-sensitive areas and increased O₃ in the VOC-sensitive areas. Heterogeneous reactions also promoted the secondary formation of fine sulfate and nitrate aerosols, especially in winter. Through the combined effect of two chemical processes, PM_2.5 can lead to a decrease in O₃ concentration of -3.3 ppb (-7.6 %), -2.2 ppb (-4.0 %), -2.9 ppb (-6.3 %), and - 5.9 ppb (-18.7 %), in spring, summer, autumn and winter in YRD. Therefore, if the PM_2.5 concentration decreases, the weakening effect of PM_2.5 on the ozone concentration will be reduced, resulting in the aggravation of ozone pollution. This study is important for understanding the synergistic pollution mechanism and provides a scientific basis for the coordinated control of urban air pollution.

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.

Aeolus winds impact on volcanic ash early warning systems for aviation

Forecasting volcanic ash atmospheric pathways is of utmost importance for aviation. Volcanic ash can interfere with aircraft navigational instruments and can damage engine parts. Early warning systems, activated after volcanic eruptions can alleviate the impacts on aviation by providing forecasts of the volcanic ash plume dispersion. The quality of these short-term forecasts is subject to the accuracy of the meteorological wind fields used for the initialization of regional models. Here, we use wind profiling data from the first high spectral resolution lidar in space, Aeolus, to examine the impact of measured wind fields on regional NWP and subsequent volcanic ash dispersion forecasts, focusing on the case of Etna's eruption on March 2021. The results from this case study demonstrate a significant improvement of the volcanic ash simulation when using Aeolus-assimilated meteorological fields, with differences in wind speed reaching up to 8 m/s when compared to the control run. When comparing the volcanic ash forecast profiles with downwind surface-based aerosol lidar observations, the modeled field is consistent with the measurements only when Aeolus winds are assimilated. This result clearly demonstrates the potential of Aeolus and highlights the necessity of future wind profiling satellite missions for improving volcanic ash forecasting and hence aviation safety.

Exposure Assessment of Ambient PM2.5 Levels during a Sequence of Dust Episodes: A Case Study Coupling the WRF-Chem Model with GIS-Based Postprocessing

A sequence of dust intrusions occurred from the Sahara Desert to the central Mediterranean in the second half of June 2021. This event was simulated by means of the Weather Research and Forecasting coupled with chemistry (WRF-Chem) regional chemical transport model (CTM). The population exposure to the dust surface PM2.5 was evaluated with the open-source quantum geographical information system (QGIS) by combining the output of the CTM with the resident population map of Italy. WRF-Chem analyses were compared with spaceborne aerosol observations derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and, for the PM2.5 surface dust concentration, with the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis. Considering the full-period (17-24 June) and area-averaged statistics, the WRF-Chem simulations showed a general underestimation for both the aerosol optical depth (AOD) and the PM2.5 surface dust concentration. The comparison of exposure classes calculated for Italy and its macro-regions showed that the dust sequence exposure varies with the location and entity of the resident population amount. The lowest exposure class (up to 5 µg m-3) had the highest percentage (38%) of the population of Italy and most of the population of north Italy, whereas more than a half of the population of central, south and insular Italy had been exposed to dust PM2.5 in the range of 15-25 µg m-3. The coupling of the WRF-Chem model with QGIS is a promising tool for the management of risks posed by extreme pollution and/or severe meteorological events. Specifically, the present methodology can also be applied for operational dust forecasting purposes, to deliver safety alarm messages to areas with the most exposed population.

Description and evaluation of a newly developed emission inventory processing system (EMIPS)

Commonly available emission inventories are often incompatible with the input requirements of atmospheric chemistry models due to temporal and spatial resolution, pollutant types, etc. We present the Emission Inventory Processing System (EMIPS) version 1, an open-source and multi-scale atmospheric emission modeling framework that prepares specific emissions inputs for atmospheric chemistry models. EMIPS is a multifunctional and user-friendly system, coded in Jython and developed on the MeteoInfo software platform. It allows users to freely combine and process emission inventories to generate model-ready emissions data. The core functions of EMIPS include preprocessing, temporal allocation, spatial allocation, chemical speciation, and vertical allocation. We detail the implementation of each function in the body of this paper, and several examples are provided for illustration. The emission outputs obtained with EMIPS have been evaluated by simulating four pollutants (PM_2.5, PM₁₀, NO₂, and O₃) concentrations in January 2017 using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), and comparison of model results with the observations indicates the model can reproduce the temporal and spatial patterns of pollutants, suggesting that EMIPS is capable of supporting atmospheric chemistry modeling. We expect this work could help to improve air quality research and forecast.

Sensitivity of WRF/Chem simulated PM2.5 to initial/boundary conditions and planetary boundary layer parameterization schemes over the Indo-Gangetic Plain

The ability of a chemical transport model to simulate accurate meteorological and chemical processes depends upon the physical parametrizations and quality of meteorological input data such as initial/boundary conditions. In this study, weather research and forecasting model coupled with chemistry (WRF-Chem) is used to test the sensitivity of PM_2.5 predictions to planetary boundary layer (PBL) parameterization schemes (YSU, MYJ, MYNN, ACM2, and Boulac) and meteorological initial/boundary conditions (FNL, ERA-Interim, GDAS, and NCMRWF) over Indo-Gangetic Plain (Delhi, Punjab, Haryana, Uttar Pradesh, and Rajasthan) during the winter period (December 2017 to January 2018). The aim is to select the model configuration for simulating PM_2.5 which shows the lowest errors and best agreement with the observed data. The best results were achieved with initial/boundary conditions from ERA and GDAS datasets and local PBL parameterization (MYJ and MYNN). It was also found that PM_2.5 concentrations are relatively less sensitive to changes in initial/boundary conditions but in contrast show a stronger sensitivity to changes in the PBL scheme. Moreover, the sensitivity of the simulated PM_2.5 to the choice of PBL scheme is more during the polluted hours of the day (evening to early morning), while that to the choice of the meteorological input data is more uniform and subdued over the day. This work indicates the optimal model setup in terms of choice of initial/boundary conditions datasets and PBL parameterization schemes for future air quality simulations. It also highlights the importance of the choice of PBL scheme over the choice of meteorological data set to the simulated PM_2.5 by a chemical transport model.

Examining the impact of dimethyl sulfide emissions on atmospheric sulfate over the continental U.S

We examine the impact of dimethylsulfide (DMS) emissions on sulfate concentrations over the continental U.S. by using the Community Multiscale Air Quality (CMAQ) model version 5.4 and performing annual simulations without and with DMS emissions for 2018. DMS emissions enhance sulfate not only over seawater but also over land, although to a lesser extent. On an annual basis, the inclusion of DMS emissions increase sulfate concentrations by 36% over seawater and 9% over land. The largest impacts over land occur in California, Oregon, Washington, and Florida, where the annual mean sulfate concentrations increase by ~25%. The increase in sulfate causes a decrease in nitrate concentration due to limited ammonia concentration especially over seawater and an increase in ammonium concentration with a net effect of increased inorganic particles. The largest sulfate enhancement occurs near the surface (over seawater) and the enhancement decreases with altitude, diminishing to 10-20% at an altitude of ~5 km. Seasonally, the largest enhancement of sulfate over seawater occurs in summer, and the lowest in winter. In contrast, the largest enhancements over land occur in spring and fall due to higher wind speeds that can transport more sulfate from seawater into land.

Vast ecosystem disturbance in a warming climate may jeopardize our climate goal of reducing CO2: a case study for megafires in the Australian 'black summer'

A warming climate is one of the most important driving forces of intensified wildfires globally. The unprecedented wildfires broke out in the Australian 'Black Summer' (November 2019-February 2020), which released massive heat, gases, and particles into the atmosphere. The total carbon dioxide (CO₂) emissions from wildfires were estimated at ∼963 million tons by using a top-down approach based on direct satellite measurements of CO₂ and fire radiative power. The fire emissions have led to an approximately 50-80 folds increase in total CO₂ emission in Australia compared with the similar seasons of 2014-2019. The excess CO₂ from wildfires has offset almost half of the global anthropogenic CO₂ emission reductions due to the Corona Virus Disease 2019 in 2020. When the wildfires were intense in December 2019, they caused a 1.48 watts per square meter additional positive radiative forcing above the monthly average in Australia and the vicinity. Our findings demonstrate that vast ecosystem disturbance in a warming climate can strongly influence the global carbon cycle and hamper our climate goal of reducing CO₂.