Atmospheric Chemistry Transport Model Research Articles

Using random forest to improve EMEP4PL model estimates of daily PM2.5 in Poland

Long-term exposure to poor air quality is responsible for many diseases and increased mortality worldwide. European Environmental Agency reports that Poland is one of the most polluted countries in Europe due to high emissions associated with large coal and wood consumption and specific weather conditions. Exceedances of WHO-recommended PM2.5 thresholds are still common in Poland, so further action is needed to protect the health of the population. Atmospheric chemical transport models (CTMs) provide information on air quality to the public and are used to regulate air pollutant emissions. However, uncertainties associated with CTMs, related to e.g. physical/chemical processes and input data quality often lead to underestimation of pollutant concentrations, especially for PM2.5, and limits the applicability of CTMs e.g. for health impact studies.A hybrid approach combining the EMEP4PL chemical transport model for Poland with the Random Forest (RF) machine learning algorithm was applied to address the limitations of CTM and reduce its underestimation. We used EMEP4PL-modelled PM2.5 concentrations for period 2016–2019 as a predictor and measured daily PM2.5 from 71 monitoring stations in Poland as a dependent variable in three RF scenarios, which differed in terms of the selected predictors. The impact of different additional variables for the area was revealed, including population and emission data, dominant type of land use, Weather and Research Forecast (WRF) meteorological parameters, and temporal patterns of PM2.5 concentrations across the years. The models were evaluated in a random 5-fold and spatial leave-one-station-out cross-validations (LOSOCV), as well as for an independent test set. Our final model achieved a test set R2 of 0.71, compared to 0.38 for EMEP4PL, along with a reduction in negative bias (0.25 μg m−3 for the final RF, compared to −11 μg m−3 for EMEP4PL) and an improved ability to detect severe PM2.5 episodes. Enhanced coefficients of determination were observed in all seasons and at all monitoring sites included in the study, for both types of cross-validation and for the test set. We estimated the contribution of each group of variables separately and discovered, that the most impactful predictors for the area of Poland included temporal patterns of PM2.5 calculated based on the averages of EMEP4PL outcome (such as day of the year, week number, etc.) and meteorological modelled factors related to temperature, planetary boundary layer height, wind speed, and atmospheric pressure. The developed approach provides a basis for improved spatiotemporal estimates and forecasting of PM2.5 in the region, which is an important step toward better understanding the impact of air pollution on the local population well-being.

A hybrid model for enhanced forecasting of PM2.5 spatiotemporal concentrations with high resolution and accuracy

Forecasting concentrations of PM2.5 is important due to its known impacts on public health and environment. However, PM2.5 concentrations can vary significantly over short distances and time, which can be influenced by local emissions and short-term weather patterns. This spatiotemporal variability makes accurate PM2.5 forecasting an inherently complex and challenging task. This study presented novel methodologies for short-term PM2.5 concentration forecast by combining the atmospheric chemistry transport model Community Multiscale Air Quality Modeling System (CMAQ) with data-driven machine learning methods, namely long short-term memory (LSTM) and random forest (RF) models. The combined model system forecast PM2.5 with 1 h, 1km × 1 km spatiotemporal resolution. The LSTM system forecast time-dependent PM2.5 concentrations at observation sites with a maximum root mean square error (RMSE) of 3.66 μg/m3 for 1-hr forecast and 23.75 μg/m3 for 72-hr forecast, leveraging results obtained from the atmospheric transport model with RMSE of 45.81 μg/m3. Wavelet transform in the LSTM system allowed learning and prediction of PM2.5 concentrations at different frequencies, capturing temporal variability of PM2.5 at various time scales. The RF model predicted distributions of PM2.5 concentrations by learning LSTM results and integrating crucial features such as CMAQ results, meteorological and topographical information. The feature significance of CMAQ results was the highest among the input features in RF models. Overall, the hybrid model could help with managing and mitigating the adverse effects of air pollution by enabling informed decision-making at the individual, community and policy levels.

Improved biomass burning emissions from 1750 to 2010 using ice core records and inverse modeling

Estimating fire emissions prior to the satellite era is challenging because observations are limited, leading to large uncertainties in the calculated aerosol climate forcing following the preindustrial era. This challenge further limits the ability of climate models to accurately project future climate change. Here, we reconstruct a gridded dataset of global biomass burning emissions from 1750 to 2010 using inverse analysis that leveraged a global array of 31 ice core records of black carbon deposition fluxes, two different historical emission inventories as a priori estimates, and emission-deposition sensitivities simulated by the atmospheric chemical transport model GEOS-Chem. The reconstructed emissions exhibit greater temporal variabilities which are more consistent with paleoclimate proxies. Our ice core constrained emissions reduced the uncertainties in simulated cloud condensation nuclei and aerosol radiative forcing associated with the discrepancy in preindustrial biomass burning emissions. The derived emissions can also be used in studies of ocean and terrestrial biogeochemistry.

Observational constraints on the parameterization of particle dry deposition over bare cropland

Dry deposition affects the removal rates and lifetimes of atmospheric particles. Bare cropland, a surface cover type representing cultivated land after harvesting and ploughing, is widespread around the world, but the parameterizations of dry deposition velocity for this land type have not been well constrained. In this study, we integrate existing observational data of particle dry deposition for bare cropland to constrain the corresponding parameterization scheme. Considering that strong disparities exist between the calculated dry deposition velocity from existing parameterizations and the observations, a range of empirical parameters employed are adjusted to best match the model simulations with the observations. The results from the revised parameterization match much more closely with the observations for the different particle sizes than those obtained when using the existing one. As a result, particles smaller than 0.5 μm in diameter exhibit a decrease in dry deposition velocity by roughly one order of magnitude, while that of larger particles increase. Among the processes contributing to the dry deposition, the impaction and interception are dominant for particles with aerodynamic diameters between 0.1 and 10 μm, due to the roughness characteristics and large clod size of bare cropland, in contrast to smooth bare soils. Applying our revised scheme in an atmospheric chemical transport model over the North China Plain leads to a general increase in PM2.5 mass concentrations across the domain, reaching up to 11.2% over the croplands. Particularly, the number concentrations of particle in Aitken and accumulation modes are increased by 12.9% and 16.8%, respectively. The associated increase in cloud condensation nuclei concentrations reaches as high as 39.6%.

Anthropogenic NO x emissions of China, the U.S. and Europe from 2019 to 2022 inferred from TROPOMI observations

Anthropogenic nitrogen oxide (NO x ) emissions are closely associated with human activities. In recent years, global human activity patterns have changed significantly owing to the COVID‐19 epidemic and international energy crisis. However, their effects on NO x emissions are not yet fully understood. In this study, we developed a two-step inversion framework using NO2 observations from the TROPOMI satellite and the GEOS-Chem global atmospheric chemical transport model, and inferred global anthropogenic NO x emissions from 2019 to 2022, focusing on China, the United States (U.S.), and Europe. Our results indicated an 1.68% reduction in NO x emissions in 2020 and a 5.72% rebound in 2021 across all regions. China rebounded faster than the others, surpassing its 2019 levels by July 2020. In 2022, emissions declined in all regions, driven mainly by the Omicron variant, energy shortages, and clean energy policies. Our findings provide valuable insights for the development of effective future emission management strategies.

Methane emissions decreased in fossil fuel exploitation and sustainably increased in microbial source sectors during 1990–2020

Methane (CH4) emission reduction to limit warming to 1.5 °C can be tracked by analyzing CH4 concentration and its isotopic composition (δ13C, δD) simultaneously. Based on reconstructions of the temporal trends, latitudinal, and vertical gradient of CH4 and δ13C from 1985 to 2020 using an atmospheric chemistry transport model, we show (1) emission reductions from oil and gas exploitation (ONG) since the 1990s stabilized the atmospheric CH4 growth rate in the late 1990s and early 2000s, and (2) emissions from farmed animals, waste management, and coal mining contributed to the increase in CH4 since 2006. Our findings support neither the increasing ONG emissions reported by the EDGARv6 inventory during 1990–2020 nor the large unconventional emissions increase reported by the GAINSv4 inventory since 2006. Total fossil fuel emissions remained stable from 2000 to 2020, most likely because the decrease in ONG emissions in some regions offset the increase in coal mining emissions in China.

Spatiotemporal atmospheric in-situ carbon dioxide data over the Indian sites-data perspective

In the current study, atmospheric carbon dioxide (CO2) data covering multiple locations in the Indian subcontinent are reported. This data was collected using a dedicated ground-based in-situ network established as part of the Geosphere-Biosphere Programme (CAP-IGBP) of the Climate and Atmospheric Processes of the Indian Space Research Organisation (ISRO). Data are collected over Ponmudi, Ooty, Sriharikota, Gadanki, Shadnagar, Nagpur, and Dehradun during 2014-2015, 2017–2020, 2012, 2011–2015, 2014–2017, 2017 and 2008–2011, respectively. The atmospheric CO2 generated as part of the CAP−IGBP network would enhance the understanding of CO2 variability in different time scales ranging from diurnal, seasonal, and annual over the Indian region. Data available under this network may be interesting to other research communities for modeling studies and spatiotemporal variability of atmospheric CO2 across the study locations. The work also evaluated the CO2 observations against the Model for Interdisciplinary Research on Climate version 4 atmospheric chemistry-transport model (MIROC4-ACTM) concentrations.

The Impact of Vertical Eddy Diffusivity Changes in the CMAQ Model on PM2.5 Concentration Variations in Northeast Asia: Focusing on the Seoul Metropolitan Area

The vertical eddy diffusion process plays a crucial role in PM2.5 prediction, yet accurately predicting it remains challenging. In the three-dimensional atmospheric chemistry transport model (3-D AQM) CMAQ, a parameter, Kz, is utilized, and it is known that PM2.5 prediction tendencies vary according to the floor value of this parameter (Kzmin). This study aims to examine prediction characteristics according to Kzmin values, targeting days exceeding the Korean air quality standards, and to derive appropriate Kzmin values for predicting PM2.5 concentrations in the DJFM Seoul Metropolitan Area (SMA). Kzmin values of 0.01, 0.5, 1.0, and 2.0, based on the model version and land cover, were applied as single values. Initially focusing on December 4th to 12th, 2020, the prediction characteristics were examined during periods of local and inflow influence. Results showed that in both periods, as Kzmin increased, surface concentrations over land decreased while those in the upper atmosphere increased, whereas over the sea, concentrations increased in both layers due to the influence of advection and diffusion without emissions. During the inflow period, the increase in vertically diffused pollutants led to increased inflow concentrations and affected contribution assessments. Long-term evaluations from December 2020 to March 2021 indicated that the prediction performance was superior when Kzmin was set to 0.01, but it was not significant for the upwind region (China). To improve trans-boundary effects, optimal values were applied differentially by region (0.01 for Korea, 1.0 for China, and 0.01 for other regions), resulting in significantly improved prediction performance with an R of 0.78, IOA of 0.88, and NMB of 0.7%. These findings highlight the significant influence of Kzmin values on winter season PM2.5 prediction tendencies in the SMA and underscore the need for considering differential application of optimal values by region when interpreting research and making policy decisions.

Revisiting the Ultraviolet Absorption Cross Section of Gaseous Nitrous Acid (HONO): New Insights for Atmospheric HONO Budget.

Nitrous acid (HONO) is an important source of hydroxyl radicals (OH) in the atmosphere. Precise determination of the absolute ultraviolet (UV) absorption cross section of gaseous HONO lays the basis for the accurate measurement of its concentration by optical methods and the estimation of HONO loss rate through photolysis. In this study, we performed a series of laboratory and field intercomparison experiments for HONO measurement between striping coil-liquid waveguide capillary cell (SC-LWCC) photometry and incoherent broadband cavity-enhanced absorption spectroscopy (IBBCEAS). Specified HONO concentrations prepared by an ultrapure standard HONO source were utilized for laboratory intercomparisons. Results show a consistent ∼22% negative bias in measurements of the IBBCEAS compared with a SC-LWCC photometer. It is confirmed that the discrepancies occurring between these techniques are associated with the overestimation of the absolute UV absorption cross sections through careful analysis of possible uncertainties. We quantified the absorption cross section of gaseous HONO (360-390 nm) utilizing a custom-built IBBCEAS instrument, and the results were found to be 22-34% lower than the previously published absorption cross sections widely used in HONO concentration retrieval and atmospheric chemical transport models (CTMs). This suggests that the HONO concentrations retrieved by optical methods based on absolute absorption cross sections may have been underestimated by over 20%. Plus, the daytime loss rate and unidentified sources of HONO may also have evidently been overestimated in pre-existing studies. In summary, our findings underscore the significance of revisiting the absolute absorption cross section of HONO and the re-evaluation of the previously reported HONO budgets.

Rapid transformation of wildfire emissions to harmful background aerosol

Wildfires are a significant source of organic aerosol during summer, with major impacts on air quality and climate. However, studies in Europe suggest a surprisingly low (less than 10%) contribution of biomass burning organic aerosol to average summertime fine particulate matter levels. In this study we combine field measurements and atmospheric chemical transport modeling, to demonstrate that the contribution of wildfires to fine particle levels in Europe during summer is seriously underestimated. Our work suggests that the corresponding contribution has been underestimated by a factor of 4–7 and that wildfires were responsible for approximately half of the total OA in Europe during July 2022. This discrepancy with previous work is due to the rapid physicochemical transformation of these emissions to secondary oxidized organic aerosol with an accompanying loss of its organic chemical fingerprints. These atmospheric reactions lead to a regionally distributed background organic aerosol that is responsible for a significant fraction of the health-related impacts caused by fine particles in Europe and probably in other continents. These adverse health effects can occur hundreds or even thousands of kilometers away from the fires. We estimate that wildfire emissions are responsible for 15–22% of the deaths in Europe due to exposure to fine particulate matter during summer.

Simulating dust emissions and secondary organic aerosol formation over northern Africa during the mid-Holocene Green Sahara period

Abstract. Paleo-proxy data indicate that a “Green Sahara” thrived in northern Africa during the early- to mid-Holocene (MH; 11 000 to 5000 years before present), characterized by more vegetation cover and reduced dust emissions. Utilizing a state-of-the-art atmospheric chemical transport model, TM5-MP, we assessed the changes in biogenic volatile organic compound (BVOC) emissions, dust emissions and secondary organic aerosol (SOA) concentrations in northern Africa during this period relative to the pre-industrial (PI) period. Our simulations show that dust emissions reduced from 280.6 Tg a−1 in the PI to 26.8 Tg a−1 in the MH, agreeing with indications from eight marine sediment records in the Atlantic Ocean. The northward expansion in northern Africa resulted in an increase in annual emissions of isoprene and monoterpenes during the MH, around 4.3 and 3.5 times higher than that in the PI period, respectively, causing a 1.9-times increase in the SOA surface concentration. Concurrently, enhanced BVOC emissions consumed more hydroxyl radical (OH), resulting in less sulfate formation. This effect counteracted the enhanced SOA surface concentration, altogether leading to a 17 % increase in the cloud condensation nuclei at 0.2 % super saturation over northern Africa. Our simulations provide consistent emission datasets of BVOCs, dust and the SOA formation aligned with the northward shift of vegetation during the “Green Sahara” period, which could serve as a benchmark for MH aerosol input in future Earth system model simulation experiments.

Photochemical ageing of aerosols contributes significantly to the production of atmospheric formic acid

Abstract. Formic acid (HCOOH) is one of the most abundant organic acids in the atmosphere and affects atmospheric acidity and aqueous chemistry. However, the HCOOH sources are not well understood. In a recent field study, we measured atmospheric HCOOH concentrations at a coastal site in southern China. The average concentrations of HCOOH were 191 ± 167 ppt in marine air masses and 996 ± 433 ppt in coastal air masses. A strong linear correlation between HCOOH concentrations and the surface area densities of submicron particulate matter was observed in coastal air masses. Post-campaign laboratory experiments confirmed that the photochemical ageing of ambient aerosols promoted by heterogeneous reactions with ozone produced a high concentration of HCOOH at a rate of 0.185 ppb h−1 under typical ambient conditions at noon. HCOOH production was strongly affected by nitrate photolysis, as this efficiently produces OH radicals that oxidise organics to form HCOOH. We incorporated this particle-phase source into a photochemical model, and the net HCOOH production rate increased by about 3 times compared with the default Master Chemical Mechanism (MCM). These findings demonstrate that the photochemical ageing of aerosols is an important source of HCOOH that should be included in atmospheric chemistry–transport models.

Trace elements in PM2.5 aerosols in East Asian outflow in the spring of 2018: emission, transport, and source apportionment

Abstract. Trace metals in aerosol particles impact Earth's radiative budget, human health, and ocean biogeochemistry. Semi-continuous measurements of the elemental composition of fine-mode (PM2.5) aerosols were conducted using an automated X-ray fluorescence analyzer on a remote island of Japan during the spring of 2018. Temporal variations in mass concentrations of geochemically important elements for this period, such as Pb, Cu, Si, Fe, and Mn, and their relationships with the emission tracers, carbon monoxide (CO) and black carbon (BC), were reported. The Integrated Massively Parallel Atmospheric Chemical Transport (IMPACT) model was used to evaluate the source apportionment of these components and was evaluated in terms of emissions and wet removal processes. Pb and Cu originated mainly from anthropogenic sources (98 % and 93 % on average, respectively) over the East Asian continent. Positive correlations of Pb and Cu with BC and CO and the similarity of their concentration-weighted trajectories indicated that the emission sources of these metals share the region where the large CO (and BC) emission sources are located and that CO can be regarded as a tracer of continental anthropogenic emissions. The air masses with minimized impacts of the wet removal during transport were extracted to elucidate the “top-down” emission ratio of Pb and Cu to CO, which were, for the first time, evaluated as 152.7 and 63.1 µg g−1, respectively, during the spring of 2018 in the East Asian outflow. The analysis of the tagged tracer simulations by the IMPACT model confirmed that BC and Si could be used as tracers for anthropogenic and dust emissions, respectively, during the observation period. The source apportionment of Fe and Mn in PM2.5 aerosols was conducted using Si and BC tracers, which revealed that the anthropogenic contribution was 17 % and 44 % on average, respectively. Based on the air mass origins of Fe and Mn, their anthropogenic fraction varied from 2 % to 29 % and 9 % to 68 %, respectively, during the high-PM2.5-concentration periods. However, despite the non-dominant anthropogenic contributions of Fe, they could adversely affect human health and ocean biogeochemistry, owing to their higher water solubility. The modeled BC, Pb, Cu, and Fe were evaluated by separately diagnosing their emission and transport. Ratios of modeled to observed concentrations for these components were analyzed in terms of the accumulated precipitation along the transport from the East Asian continent. The current model simulations were found to overestimate the emissions (based on the Community Emissions Data System, CEDS v2021-02-05) of BC by 44 % and underestimate Cu by 45 %, anthropogenic Fe by 28 % in East Asia, and the wet deposition rates for BC and Pb. Overall, Cu in East Asia exhibited a different nature from BC and Pb in terms of emission sources and wet removal.

Simulating impacts on UK air quality from net-zero forest planting scenarios

Abstract. The UK proposes additional bioenergy plantations and afforestation as part of measures to meet net-zero greenhouse gas emissions, but species and locations are not yet decided. Different tree species emit varying amounts of isoprene and monoterpene volatile organic compounds that are precursors to ozone and secondary organic aerosol (SOA) formation, the latter of which is a component of PM2.5. The forest canopy also acts as a depositional sink for air pollutants. All these processes are meteorologically influenced. We present here a first step in coupling information on tree species planting suitability and other planting constraints with data on UK-specific BVOC emission rates and tree canopy data to simulate, via the WRF-EMEP4UK high spatial-resolution atmospheric chemistry transport model, the impact on UK air quality of four potential scenarios. Our “maximum planting” scenarios are based on planting areas where yields are predicted to be ≥ 50 % of the maximum from the Ecological Site Classification decision support system (ESC DSS) for Eucalyptus gunnii, hybrid aspen (Populus tremula), Italian alder (Alnus cordata) and Sitka spruce (Picea sitchensis). The additional areas of forest in our scenarios are 2.0 to 2.7 times the current suggestions for new bioenergy and afforestation land cover in the UK. Our planting scenarios increase UK annual mean surface ozone concentrations by 1.0 ppb or 3 % relative to the baseline land cover for the highest BVOC-emitting species (e.g. E. gunnii). Increases in ozone reach 2 ppb in summer when BVOC emissions are greatest. In contrast, all the additional planting scenarios lead to reductions in UK annual mean PM2.5 – ranging from −0.2 µg m−3 (−3 %) for Sitka spruce to −0.5 µg m−3 (−7 %) for aspen – revealing that PM2.5 deposition to the additional forest canopy area more than offsets additional SOA formation. Relative decreases in annual mean PM2.5 are greater than the relative increases in annual mean ozone. Reductions in PM2.5 are least in summer, coinciding with the period of maximum monoterpene emissions. Although only a first step in evaluating the impact of increased forest plantation on UK air quality, our study demonstrates the need for locally relevant data on land cover suitability, emissions and meteorology in model simulations.

Contrasting changes in ozone during 2019–2021 between eastern and the other regions of China attributed to anthropogenic emissions and meteorological conditions

Ozone pollution is one of the most severe air quality issues in China that poses a serious threat to human health and ecosystems. During 2019–2021, the maximum daily 8-h average ozone concentrations in eastern China (110–122.5°E, 26–42°N) and the rest of China (ROC) show different decreasing patterns, with ozone concentrations in eastern China decreasing by 14.9 μg/m3, which is much larger than 4.8 μg/m3 in ROC. Here, based on two independent methods, the atmospheric chemical transport model (GEOS-Chem) simulations and the machine learning (ML) model (LightGBM) predictions, the reasons for the differences in ozone changes between eastern China and ROC during the warm season (April to September) are investigated. According to the GEOS-Chem (LightGBM) results, changes in the meteorological conditions contributed to an ozone decrease by 7.3 (6.8) μg/m3 in eastern China due to decreased chemical production and an ozone decrease by 6.8 (7.0) μg/m3 in ROC attributed to the weakened horizontal and vertical advection. With the influence of meteorological factors excluded, the observations show that changes in anthropogenic emissions resulted in an ozone decrease by 7.6 (8.1) μg/m3 in eastern China and an ozone increase by 2.0 (2.2) μg/m3 in ROC, which is primarily induced by the changes in NOx emissions. The surface measurements and satellite retrievals also indicate that the reduction in NOx emissions in ROC is less efficient than that in the more developed eastern China, leading to contrasting changes in ozone concentrations between eastern China and ROC during 2019–2021. Our results highlight the critical need to reduce ozone precursor emissions in the rest regions of China apart from eastern China.

Effects of simulated secondary organic aerosol water on PM1 levels and composition over the US

Abstract. Water is a key component of atmospheric aerosol, affecting many aerosol processes including gas-to-particle partitioning of semi-volatile compounds. Water related to secondary organic aerosol (SOAW) is often neglected in atmospheric chemical transport models and is not considered in gas-to-particle partitioning calculations for inorganic species. We use a new inorganic aerosol thermodynamics model, ISORROPIA-lite, which considers the effects of SOAW, to perform chemical transport model simulations for 1 year over the continental United States to quantify its effects on aerosol mass concentration and composition. SOAW can increase average fine aerosol water levels by up to a factor of 2 when secondary organic aerosol (SOA) is a major PM1 component. This is often the case in the south-eastern US, where SOA concentrations are higher. Although the annual average impact of this added water on total dry PM1 concentrations due to increased partitioning of nitrate and ammonium is small (up to 0.1 µg m−3), total dry PM1 increases of up to 2 µg m−3 (with nitrate levels increases of up to 200 %) can occur when RH levels and PM1 concentrations are high.

Application of ADMS-Urban for an area with a high contribution of residential heating emissions - model verification and sensitivity study for PM2.5

Air pollution poses a significant risk to both human health and the environment in the contemporary world. Among the various pollutants, particulate matter with a diameter <2.5 μm (PM2.5) is regarded as the most hazardous. It has been implicated in over four million global fatalities in 2019 alone. This research paper divulges the outcomes of modelling the spatial-temporal fluctuations of PM2.5 concentrations within the confines of Wroclaw, a city situated in Poland, Central Europe. The model's output was evaluated through comparison with collected data from two government-operated monitoring stations within the city. For this study, we used the ADMS-Urban model and tested two different sources of background data (low-cost sensors and the EMEP MSC-W atmospheric chemistry transport model). The statistical analysis conducted in the paper indicates that the model reproduces the temporal variability of PM2.5. The conclusions from this research indicate that the average annual PM2.5 concentration within Wroclaw is 13.8 μg/m3, with the concentration peaking in the month of March. The spatial distribution reveals the highest PM2.5 concentrations primarily in the southern and western zones of the city, with additional elevated concentrations observed sporadically throughout the city. The study unveils that 1.3 % of Wroclaw's area experiences PM2.5 concentrations exceeding the EU's annual limit of 20 μg/m3. When considered in relation to the WHO's suggested annual average level of 5 μg/m3, Wroclaw city experiences exceedances throughout. When background concentrations are excluded from the model, the annual average PM2.5 concentration across the city is noted to be reduced by >50 %. A thorough investigation of the city's emission structure, taking into account only emissions from the city without background, indicates that the residential sector contributes about 77.3 % of the total annual average PM2.5 concentration in Wroclaw. The transportation and industrial sectors account for nearly 19.5 % and 3.2 %, respectively.

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

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

Integrated air quality modeling for urban policy: A novel approach with olympus-chimere

This paper presents a novel approach to air quality modeling for megacities, focusing on the Paris region for the year 2009. The simulation is conducted by coupling the transport, energy demand, and emission model OLYMPUS with the atmospheric chemical transport model CHIMERE. OLYMPUS's emission calculations are based on the representation of an urban configuration, the simulation of a synthetic population, and the calculation of transport demand and energy consumption in buildings using an activity-based statistical approach. Emissions derived from this simulation, along with biogenic and additional industrial emissions, were used as input data in the CHIMERE model to predict pollutant concentrations. CHIMERE outputs were compared with observations from the local AIRPARIF air quality monitoring network, and with a reference simulation conducted using a benchmark bottom-up emission inventory. The results indicate that the platform provides a comprehensive representation of emissions and the resulting air quality. They also highlight the excellent representation of the spatial and temporal distribution of urban pollutant concentrations in comparison with both model and observational data. OLYMPUS tends to emit more primary pollutants than the reference emission inventory in the dense urban center. However, this often improves model scores for NO2, and these deviations remain within the uncertainty margins set for emission inventories. The approach to emission production, rooted in the connection between urban configuration and individual behaviors, allows for the exploration of innovative air quality scenarios centered on energy consumption practices. The validation of our results furnishes the OLYMPUS-CHIMERE coupling with a dependable framework for addressing numerous research inquiries related to the impact of urban policies on air quality.

Early life PM2.5 exposure, childhood cognitive ability and mortality between age 11 and 86: A record-linkage life-course study from Scotland

BackgroundLiving in areas with high air pollution concentrations is associated with all-cause and cause-specific mortality. Exposure in sensitive developmental periods might be long-lasting but studies with very long follow-up are rare, and mediating pathways between early life exposure and life-course mortality are not fully understood. MethodsData were drawn from the Scottish Longitudinal Study Birth Cohort of 1936, a representative record-linkage study comprising 5% of the Scottish population born in 1936. Participants had valid age 11 cognitive ability test scores along with linked mortality data until age 86. Fine particle (PM2.5) concentrations estimated with the EMEP4UK atmospheric chemistry transport model were linked to participants’ residential address derived from the National Identity Register in 1939 (age 3). Confounder-adjusted Cox regression estimated associations between PM2.5 and mortality; regression-based causal mediation analysis explored mediation through childhood cognitive ability. ResultsThe final sample consisted of 2734 individuals with 1608 deaths registered during the 1,833,517 person-months at risk follow-up time. Higher early life PM2.5 exposure increased the risk of all-cause mortality (HR = 1.03, 95% CI: 1.01–1.04 per 10 μg m−3 increment), associations were stronger for mortality between age 65 and 86. PM2.5 increased the risk of cancer-related mortality (HR = 1.05, 95% CI: 1.02–1.08), especially for lung cancer among females (HR = 1.11, 95% CI: 1.02–1.21), but not for cardiovascular and respiratory diseases. Higher PM2.5 in early life (≥50 μg m−3) was associated with lower childhood cognitive ability, which, in turn, increased the risk of all-cause mortality and mediated 25% of the total associations. ConclusionsIn our life-course study with 75-year of continuous mortality records, we found that exposure to air pollution in early life was associated with higher mortality in late adulthood, and that childhood cognitive ability partly mediated this relationship. Findings suggest that past air pollution concentrations will likely impact health and longevity for decades to come.