Near-surface Concentrations Research Articles

Estimating Daily Concentrations of Near-Surface CO, NO2, and O3 Simultaneously Over China Based on Spatiotemporal Multi-Task Transformer Model

Accurate and efficient estimation of near-surface air pollutant concentrations holds significant practical importance. Current models for estimating near-surface concentrations (NSC) primarily rely on shallow methods and focus on estimating a single pollutant. However, these models face challenges in capturing the complex spatiotemporal patterns of NSC and demonstrate inefficiency. To overcome these limitations, we propose a spatiotemporal multi-task Transformer model (stmtTransformer) to simultaneously estimate the NSC of carbon monoxide (CO), nitrogen dioxide (NO2), and ozone (O3). Estimation experiments conducted in China from 2021 to 2022 demonstrate that stmtTransformer achieves optimal performance by effectively capturing the spatiotemporal variations of NSC. Based on sample-based validation, the R2 values are 0.643 (CO), 0.781 (NO2), and 0.902 (O3), and the RMSE values are 0.194 mg/m3 (CO), 5.613 µg/m3 (NO2), and 13.330 µg/m3 (O3), respectively. In terms of efficiency, stmtTransformer significantly improved the training efficiency by 185.21 % and the estimation efficiency by 129.44 % compared to the single-task model. Finally, when plotting the daily and seasonal maps of NSC for 2022, it is evident that the estimates exhibit a consistent spatial distribution.

Estimate of near-surface NO2 concentrations in Fenwei Plain, China, based on TROPOMI data and random forest model.

Nitrogen dioxide (NO2) concentration is a crucial indicator of ground-level air quality, and elevated concentrations can adversely affect human health and the atmospheric environment. In this study, we utilized Tropospheric Monitoring Instrument (TROPOMI) tropospheric NO2 vertical column density data (VCD) and multi-source geographic data to establish a random forest regression (RF) model that accurately estimates NO2 concentrations near the ground in the Fenwei Plain. The model addresses the inherent limitations of traditional ground-based monitoring and provides data support for analyzing regional pollution spatial and temporal characteristics. (1) The RF model based on TROPOMI and geographic data demonstrates high estimation accuracy, with monthly average RF model fit and validation coefficient of determination (R2) reaching 0.949 and 0.875, respectively. (2) A complex nonlinear relationship exists between near-surface NO2 concentration and multi-source geographic data. The RF model's estimations reveal clear seasonal and regional variations in near-surface NO2 concentration. Concentrations are generally highest in winter, followed by spring and autumn, and lowest in summer. The high NO2 concentrations are primarily mainly distributed in the plains and river valleys with low elevation and dense population density. The model estimation results also indicate that the estimated effect is better when the NO2 concentration fluctuates less and anthropogenic emission reduction measures significantly impact the NO2 concentration near the ground. (3) The population exposure risk results indicate that most cities in the Fenwei Plain face varying exposure risks. These findings offer valuable insights for regional NO2 pollution management.

Contributions of biomass burning in 2019 and 2020 to Arctic black carbon and its transport pathways

The circum-Arctic region experienced serious fires in 2019 and 2020. Biomass burning is considered a primary source of black carbon (BC) aerosols. BC contributes to Arctic warming and impacts snow/ice melting. However, the impacts of biomass burning on BC in the Arctic during these recent serious fires have not been quantified in detail. In this study, based on numerical simulations using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), we calculated the contribution ratios of biomass burning to Arctic BC and revealed its transport pathways. Affected by biomass burning emissions, the near-surface BC concentrations over the terrestrial areas within the Arctic Circle were highest in summer, and declined in spring and autumn. Spatially, high-concentration levels of BC were distributed in the Russian central and eastern areas. Biomass burning accounted for 63.72% and 45.18% of the summer BC in the Arctic near-surface and middle troposphere, respectively. In the near-surface, the contributions from local Arctic sources were comparable to those from sources outside the Arctic Circle in summer. In the middle troposphere, contributions from sources outside the Arctic Circle were dominant. In summer, BC originating from biomass burning in Siberia was transported a short distance by southwesterly winds to the central Arctic near-surface, while the enhanced southwesterly winds in the middle troposphere transported BC from Siberia, Alaska and northern Canada to the central Arctic and Greenland. In spring and autumn, most BC originating from biomass burning in the near-surface of Eurasia was transported eastward by westerly winds and then transported northward over the North Pacific Ocean, and the long distance may have resulted in fewer effects on the Arctic. These results highlight the important role of biomass burning in the Arctic environment under a warming climate.

Near-Surface Concentration of CH4, СО2, СО, and δ13C–СH4 in the Air Based on the Observations at the Station of the Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences, in Moscow

Near-Surface Concentration of CH4, СО2, СО, and δ13C–СH4 in the Air Based on the Observations at the Station of the Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences, in Moscow

Dust source tracing and their transport mechanisms before monsoon season from three glaciers at the Tibetan Plateau: revelations from REE analysis and an indirect tracing approach.

Understanding the origins of Tibetan Plateau (TP) glacier dust is vital for glacier dynamics and regional climate understanding. In May 2016, snow pit samples were collected from glaciers on the TP: Qiyi (QY) in the north, Yuzhufeng (YZF) in the center, and Xiaodongkemadi (XDK) in the south. Rare earth element (REE) concentrations were analyzed using inductively coupled plasma mass spectrometry (ICP-MS), and near-surface PM10 concentrations were extracted from a dataset of Chinese near-surface PM10. Two tracing approaches were used: direct REE tracing and an indirect approach combining potential source contribution function (PSCF) and concentration-weighted trajectory (CWT). Both methods yielded consistent results. Pre-monsoon, TP surface soils, Taklimakan Desert, and Qaidam Basin contributed to glacier dust. Notably, central and southern glaciers showed Thar Desert influence, unlike the northern ones. Taklimakan and Thar Deserts were major contributors due to their substantial contribution and high dust concentration. Taklimakan dust, influenced by terrain and westerly winds, affected central and southern glaciers more than northern ones. Westerlies carried Thar Desert dust to the TP after it was uplifted by updrafts in northwest India, significantly affecting southern glaciers. Furthermore, comparing the two tracer methods, the indirect approach combining PSCF and CWT proved more effective for short-term dust source tracing.

Impacts of urban expansion on meteorology and air quality in North China Plain during wintertime: A case study

The North China Plain (NCP) is one of the areas with a fast urbanization rate. The proportion of urban areas has increased from 1.78% in 2000 to 6.70% in 2015 in the NCP. Urban expansion induce variations in air pollutants are mainly determined by changes in meteorological conditions and atmospheric physicochemical processes. A persistent severe wintertime particulate pollution episode is simulated using the WRF-Chem model to investigate the impacts of land use/cover change on air quality in the NCP. Sensitivity simulations demonstrated that the urban expansion increases the near-surface temperature by approximately 0.2 °C, and further the development of the planetary boundary layer height by approximately 1.6%, but decreases the relative humidity by 0.4% on average. The near-surface O3 concentrations are increased by 2.2%, but the PM2.5 concentrations are decreased by 2.4% on average in the NCP. The near-surface concentration of NO2, SO2, and CO decrease by 4.5%, 2.2%, and 2.7%, respectively. The decrease in PM2.5 concentrations is approximately 8.9% in urban areas. Although urban expansion increases anthropogenic emissions of air pollutants, it alleviates particulate pollution to some degree, particularly in urban areas.

Spatiotemporal modeling of air pollutant concentrations in Germany using machine learning

Abstract. Machine learning (ML) models are becoming a meaningful tool for modeling air pollutant concentrations. ML models are capable of learning and modeling complex nonlinear interactions between variables, and they require less computational effort than chemical transport models (CTMs). In this study, we used gradient-boosted tree (GBT) and multi-layer perceptron (MLP; neural network) algorithms to model near-surface nitrogen dioxide (NO2) and ozone (O3) concentrations over Germany at 0.1∘ spatial resolution and daily intervals. We trained the ML models using TROPOspheric Monitoring Instrument (TROPOMI) satellite column measurements combined with information on emission sources, air pollutant precursors, and meteorology as feature variables. We found that the trained GBT model for NO2 and O3 explained a major portion of the observed concentrations (R2=0.68–0.88 and RMSE=4.77–8.67 µg m−3; R2=0.74–0.92 and RMSE=8.53–13.2 µg m−3, respectively). The trained MLP model performed worse than the trained GBT model for both NO2 and O3 (R2=0.46–0.82 and R2=0.42–0.9, respectively). Our NO2 GBT model outperforms the CAMS model, a data-assimilated CTM but slightly underperforms for O3. However, our NO2 and O3 ML models require less computational effort than CTM. Therefore, we can analyze people's exposure to near-surface NO2 and O3 with significantly less effort. During the study period (30 April 2018 and 1 July 2021), it was found that around 36 % of people lived in locations where the World Health Organization (WHO) NO2 limit was exceeded for more than 25 % of the days during the study period, while 90 % of the population resided in areas where the WHO O3 limit was surpassed for over 25 % of the study days. Although metropolitan areas had high NO2 concentrations, rural areas, particularly in southern Germany, had high O3 concentrations. Furthermore, our ML models can be used to evaluate the effectiveness of mitigation policies. Near-surface NO2 and O3 concentration changes during the 2020 COVID-19 lockdown period over Germany were indeed reproduced by the GBT model, with meteorology-normalized near-surface NO2 having significantly decreased (by 23±5.3 %) and meteorology-normalized near-surface O3 having slightly increased (by 1±4.6 %) over 10 major German metropolitan areas when compared to 2019. Finally, our O3 GBT model is highly transferable to neighboring countries and locations where no measurements are available (R2=0.87–0.94), whereas our NO2 GBT model is moderately transferable (R2=0.32–0.64).

Shifting summer holidays in Spain as an adaptation measure to climate change

This paper assesses whether moving summer holidays to the warmest period of the year in Spain could be a useful climate change adaptation strategy. While the most popular period for Spanish summer holidays has traditionally been August, we illustrate that the second half of July is the hottest period of the year and when the negative effects of high temperatures are most pronounced. If the holiday period in the second fortnight of August was moved to the second fortnight of July, some of the associated impacts would be mitigated due to the reduced anthropogenic activity during non-working days. In particular, we find a significant reduction in the annual peak of labour productivity loss (~25 %) and, to a lesser extent, of electricity demand and near-surface ozone concentrations (~3–4 %). Finally, we also show that global warming could lead to enhanced differences between both fortnights (even with no change in the seasonal cycle of temperature) because of the non-linear relationships between temperature and its impacts. Therefore, the positive effect of shifting holidays would be even larger in the coming future.

The Spatiotemporal Distribution of NO2 in China Based on Refined 2DCNN-LSTM Model Retrieval and Factor Interpretability Analysis

With the advancement of urbanization in China, effective control of pollutant emissions and air quality have become important goals in current environmental management. Nitrogen dioxide (NO2), as a precursor of tropospheric ozone and fine particulate matter, plays a significant role in atmospheric chemistry research and air pollution control. However, the uneven ground monitoring stations and low temporal resolution of polar-orbiting satellites set challenges for accurately assessing near-surface NO2 concentrations. To address this issue, a spatiotemporal refined NO2 retrieval model was established for China using the geostationary satellite Himawari-8. The spatiotemporal characteristics of NO2 were analyzed and its contribution factors were explored. Firstly, seven Himawari-8 channels sensitive to NO2 were selected by using the forward feature selection based on information entropy. Subsequently, a 2DCNN-LSTM network model was constructed, incorporating the selected channels and meteorological variables as retrieval factors to estimate hourly NO2 in China from March 2018 to February 2020 (with a resolution of 0.05°, per hour). The performance evaluation demonstrates that the full-channel 2DCNN-LSTM model has good fitting capability and robustness (R2 = 0.74, RMSE = 10.93), and further improvements were achieved after channel selection (R2 = 0.87, RMSE = 6.84). The 10-fold cross-validation results indicate that the R2 between retrieval and measured values was above 0.85, the MAE was within 5.60, and the RMSE iwas within 7.90. R2 varied between 0.85 and 0.90, showing better validation at mid-day (R2 = 0.89) and in spring and fall transition seasons (R2 = 0.88 and R2 = 0.90). To investigate the cooperative effect of meteorological factors and other air pollutants on NO2, statistical methods (beta coefficients) were used to test the factor interpretability. Meteorological factors as well as other pollutants were analyzed. From a statistical perspective, PM2.5, boundary layer height, and O3 were found to have the largest impacts on near-surface NO2 concentrations, with each standard deviation change in these factors leading to 0.28, 0.24, and 0.23 in standard deviations of near-surface NO2, respectively. The findings of this study contribute to a comprehensive understanding of the spatiotemporal distribution of NO2 and provide a scientific basis for formulating targeted air pollution policies.

The global impact of the transport sectors on the atmospheric aerosol and the resulting climate effects under the Shared Socioeconomic Pathways (SSPs)

Abstract. A global aerosol–climate model is applied to quantify the impact of the transport sectors (land transport, shipping, and aviation) on aerosol and climate. Global simulations are performed for the present day (2015), based on the emission inventory of the Climate Model Intercomparison Project Phase 6 (CMIP6), and for near-term (2030) and mid-term (2050) future projections, under the Shared Socioeconomic Pathways (SSPs). The results for the present day show that land transport emissions have a large impact on near-surface concentrations of black carbon and aerosol nitrate over the most populated areas of the globe, but with contrasting patterns in terms of relative contributions between developed and developing countries. In spite of the recently introduced regulations to limit the fuel sulfur content in the shipping sector, shipping emissions are still responsible for a considerable impact on aerosol sulfate near-surface concentrations, about 0.5 to 1 µg m−3 in the most travelled regions, with significant effects on continental air pollution and in the northern polar regions as well. Aviation impacts on aerosol mass are found to be quite small, of the order of a few nanograms per cubic metre, while this sector considerably affects particle number concentrations, contributing up to 20 %–30 % of the upper-tropospheric particle number concentration at the northern mid-latitudes. The transport-induced impacts on aerosol mass and number concentrations result in a present-day radiative forcing of −164, −145, and −64 mW m−2 for land transport, shipping, and aviation, respectively, with a dominating contribution by aerosol–cloud interactions. These forcings represent a marked offset to the CO2 warming from the transport sectors and are therefore very relevant for climate policy. The projections under the SSPs show that the impact of the transport sectors on aerosol and climate are generally consistent with the narratives underlying these scenarios: the lowest impacts of transport on both aerosol and climate are simulated under SSP1, especially for the land transport sector, while SSP3 is generally characterized by the largest effects. Notable exceptions to this picture, however, exist, as the emissions of other anthropogenic sectors also contribute to the overall aerosol concentrations and thus modulate the relevance of the transport sectors in the different scenarios, not always consistently with their underlying storyline. On a qualitative level, the results for the present day mostly confirm the findings of our previous assessment for the year 2000, which used a predecessor version of the same model and the CMIP5 emission data. Some important quantitative differences are found, which can mostly be ascribed to the improved representation of aerosol background concentrations in the present study.

Exploring high-resolution near-surface CO concentrations based on Himawari-8 top-of-atmosphere radiation data: Assessing the distribution of city-level CO hotspots in China

Carbon monoxide (CO) has notable effects on the atmospheric environment and human health. This study used Himawari-8 top-of-atmosphere radiation (TOAR) data, meteorological factors, and geographic information, combined with an interpretable deep forest (DF) model, to obtain high-spatiotemporal resolution near-surface CO concentrations (temporal resolution: 1 h; spatial resolution: 0.05°) in China from September 2015 to August 2021 and reveal the distribution and variation in city-level CO hotspots. Based on the feature importance method and the spectral absorption characteristics of pollutants, the optimal combination of Himawari-8 band for the TOAR-CO model was selected. The performance comparison of five machine learning models showed that the DF model was the most performant. The hourly average R2 value (using 10-fold cross-validation) of the TOAR-CO model ranged from 0.72 to 0.78 (10:00 to 15:00, Beijing time); the R2 values of daily, monthly, and annual averages were 0.79, 0.97 and 0.98, respectively. The feature importance of the model varied in the different seasons and regions, with TOAR and meteorological factors had significantly contributions to the model. During the study period, the CO concentration in most areas of China showed a decreasing trend, with an average decline rate of 7.2%·yr−1. Despite CO being a stable, long-lived gas, the TOAR-CO model still captured obvious intraday variation in CO, which reached a peak of 0.89 mg/m3 at 10:00 and dropped to 0.69 mg/m3 at 15:00. The spatiotemporal distribution of estimated CO was basically consistent with satellite and reanalysis data. The TOAR-CO model predictions reflected city-level CO pollution, and the distribution of CO hotspots was closely related to industrial structure and human activities. During the COVID-19 pandemic, CO concentration significantly dropped after the 4th week of control. The CO concentrations in the hotspot cities decreased more significantly, by over 50%. The research results provide some supports for the assessment of CO fine-scale pollution and related research.

Rebuilding high-quality near-surface ozone data based on the combination of WRF-Chem model with a machine learning method to better estimate its impact on crop yields in the Beijing-Tianjin-Hebei region from 2014 to 2019

In recent years, the problem of surface ozone pollution in China has been of great concern. According to observation data from monitoring stations, the concentration of near-surface ozone (O3) in China has gradually increased in recent years, and ozone concentration often exceeds the contaminant limit standard, especially in the Beijing-Tianjin-Hebei (BTH) region. High O3 concentration pollution will adversely affect crop growth, which can cause crop yield losses. Therefore, it is urgent to recognize the situation of ozone pollution in the BTH region and quantitatively evaluate the crop yield losses caused by ozone pollution to develop more effective pollution prevention and control policies. However, the monitoring of ozone concentration in China started relatively late compared with some developed countries, and currently, long-time series data covering the BTH region cannot be obtained, which makes it difficult to evaluate the impact of ozone on crop yield. Therefore, a new method (WRFC-XGB) was proposed in this study to establish a high-precision near-surface O3 concentration dataset covering the whole BTH region from 2014 to 2019 by integrating the Weather Research and Forecasting with Chemistry (WRF-Chem) model with the extreme gradient boosting (XGBoost) machine learning algorithm. Through verification with ground observation station data, the results of WRFC-XGB are satisfactory, and R2 can reach 0.78–0.91. Compared with other algorithms, the accuracy of the near-surface ozone concentration dataset is greatly improved, which can be used to estimate the impact of surface ozone on crop yield. Based on this dataset, the yield loss of winter wheat, rice, and maize caused by O3 pollution was estimated by using the response equation of the relative yield and ozone dose index. The results showed that the total yield losses of winter wheat, rice and maize from 2014 to 2019 were 2659.21 million tons, 49.23 million tons and 1721.56 million tons due to ozone pollution in the BTH region, respectively, and the highest relative yield loss of crops caused by O3 pollution could be 29.37% during 2014–2019, which indicated that the impact of ozone pollution on crop yield cannot be ignored, and effective measures need to be developed to control ozone pollution, prevent crop production loss, and ensure people's food security.

Spatiotemporal variability of near-surface CO2 and its affecting factors over Mongolia

We investigate the spatiotemporal variability of near-surface CO2 concentrations in Mongolia from 2010 to 2019 and the factors affecting it over four climate zones of Mongolia based on the Köppen-Geiger climate classification system, including arid desert climate (BWh), arid steppe climate (BSk), dry climate (Dw), and polar frost climate (ET). Initially, we validate the near-surface CO2 datasets obtained from the Greenhouse Gases Observing Satellite (GOSAT) using ground-based CO2 observations obtained from the World Data Center for Greenhouse Gases (WDCGG) and found good agreement. The results showed that CO2 concentrations over Mongolia steadily increased from 389.48 ppmv in 2010 to 409.72 ppmv in 2019, with an annual growth rate of 2.24 ppmv/year. Spatially, the southeastern Gobi desert region has the highest annual average CO2 concentration, while the northwestern Alpine and Meadow steppe region exhibits the most significant growth rate. Additionally, significant monthly and seasonal variations were observed in each climate zone, with CO2 levels decreasing to a minimum in summer and reaching a maximum in spring. Furthermore, our findings revealed a negative correlation between CO2 concentrations and vegetation parameters (NDVI, GPP, and LAI) during summer when photosynthesis is at its peak, while a positive correlation was observed during spring and autumn when the capacity for carbon sequestration is lower. Understanding CO2 concentrations in different climate zones and the uptake capacity of vegetation may help improve estimates of carbon sequestration in ecosystems such as deserts, steppes and forests.

Spatiotemporal dynamics of near-surface ozone concentration and potential source areas in northern China during 2015-2020.

Near-surface ozone (O3) pollution has become one of the main factors hampering urban air quality in northern China. However, on a spatiotemporal scale, dynamic transport paths and potential source areas of O3 in northern China are ambiguous. In addition, we suspect that the contribution of transportation activities to urban O3 concentrations developed in northern China may be underestimated. In this study, the HYSPLIT, PSCF, CWT and GTWR model were used to study the transmission paths, potential source areas and driving factors of urban O3 concentration on a spatiotemporal scale. The average annual concentration of surface O3 (the 90th percentile of MDA8) was 172 ± 29μg/m3 in northern China from 2015 to 2020. In terms of inter-annual variation, the urban O3 concentration increased from 2015 to 2018, and decreased after 2018. On the spatial scale, the areas with high O3 concentration were mainly clustered in industrial cities (Tangshan, Baoding, Shijiazhuang, Xingtai and Handan). During the study period, the area with high O3 concentration in northern China shifted from northwest to southeast. From 2015 to 2020, the influence of long-distance air mass trajectories from Xinjiang and Siberi on airflow transport in Beijing city dominates (78.60%) The average percentage of short-distance transport trajectories from Shandong Peninsula region is about 21.40%. The core potential source areas of O3 pollution shifted from northwest to southeast, but the contribution to O3 pollution in Beijing gradually weakened during the same period. Temperature and relative humidity were the main meteorological driving factors affecting O3 concentration in the study area, while population density, the proportion of secondary industry in GDP, industrial smoke (dust) emissions, and passenger traffic were the main non-meteorological factors. During the period study, the influence of industrial and traffic emissions had a more significant impact on O3 concentration in northern China, which will require that more attention be paid to emission mitigation in the regional industrial and passenger transportation sector, as well as the joint prevention and control of O3 pollution in northern China in the future.

DC resistivity and gravimetry to improve analysis and assessment in phosphate exploration: Serra da Bodoquena, Western Mato Grosso do Sul state – Brazil

Phosphatic zones with high exploration potential were identified in the mid-western area near Bonito municipality, Mato Grosso do Sul state, Brazil. This resource consists of a significant matter in reactive natural phosphate production representing an important element to increase agriculture in the country. The present study proposes an unusual geophysical approach as a method to identify and delineate rocks related to P2O5 enrichment in the Ressaca target located in Bonito municipality, Mato Grosso do Sul State, Brazil. DC resistivity and gravimetric methods integrated with detailed surface geologic mapping were applied to detect lithologic heterogeneities near-surface and in-depth. The materials and their respective physical properties are related to weathered alteration zones (supergene) and phosphatic dolomitic breccias associated with the highest phosphatic concentration zones according to previous geologic prospective surveys. The resistivity and gravimetry results along with E-W oriented profiles detected significant anomalies concentrated in a zone in the mid-portion of the acquisition area. Resistivity, Bouguer, and Residual data integration allowed us to define circular features related to the enriched breccias and supergene ore. Results also define a near-surface concentration zone representing a useful tool to assess and help the subsurface analysis leading to an increase in the prospective survey planning (drilling) and the company's budget.

A Preliminary Analysis of Typical Structures and Microphysical Characteristics of Precipitation in Northeastern China Cold Vortexes

The northeastern China cold vortex (NCCV) is the main weather system affecting Northeast China. Based on the precipitation products from the dual-frequency precipitation radar (DPR) onboard the Global Precipitation Measurement core observatory (GPM) satellite, the precipitation structures and microphysical properties for different rain types in 6432 NCCVs from 2014 to 2019 were studied using dynamic composite analysis. Our results show that the precipitation in NCCVs is dominated by stratiform precipitation. Regions with high stratiform and convective precipitation frequency have a comma shape. The growth mechanism of precipitation particles changes at ~4 km in altitude, the lower particles grow through collision (more pronounced in convective precipitation), and the upper hydrometeors grow through the Bergeron process. Additionally, the precipitation structures and microphysical properties exhibit great regional variations in NCCVs. The rainfall for all rain types is the strongest in the southeast region within an NCCV, mainly characterized by higher near-surface droplet concentration, while precipitation events occur more frequently in the southeast region for all rain types. There are active rimming growth processes above the melting layer for convective precipitation in the western region of an NCCV. In the southeast region of an NCCV, the collision growth of droplets in both types of precipitation is the most obvious.

Integration of Satellite Monitoring and Mathematical Modeling in the Analysis of the Nature of Elevated Sulfur Dioxide Concentrations in the Surface Air of the Northern Part of Finland

The article is devoted to the investigation of the nature of the increment in surface concentration of toxic gas sulfur dioxide (SO2) in Northern Finland. The study is carried out for June 2011 when the increased near-surface SO2 concentration was registered at Finnish observation station Inari Raja-Jooseppi together with the surface wind blowing from the territory of the Kola Peninsula. The hypothesis of natural increment of near-surface SO2 concentration as a result of Grímsvötn volcano eruption (Iceland) is verified using satellite SO2 observations by OMI, numerical weather prediction and atmospheric chemistry model Enviro-HIRLAM and particle dispersion model HYSPLIT. The results show that not only local man-made sources, but also the remote transfer of pollutants from volcanic eruptions can cause an increased near-surface SO2 concentration. To verify this hypothesis, a large set of statistics is required in Northern Finland during periods of volcanic activity in Iceland.

Relationship between summer time near-surface ozone concentration and planetary boundary layer height in Beijing

The planetary boundary layer height (PBLH) is critical in affecting the concentration of air pollutants at surface. The relationship between PBLH and surface ozone concentration can be complicated but not entirely clear. In this study, by using observations and WRF-Chem simulations, we explore the relationship between the surface ozone and the PBLH in the summer daytime in Beijing, a major global ozone pollution hotspot. It is found that the surface ozone-PBLH relationship exhibits a three-stage evolution with ozone: first, increasing with PBLH, then keeping steady, and finally falling. The two turning points are around 1000 and 2000 m respectively. This process is likely controlled by the different contributions of chemical production and vertical mixing. Specifically, when PBLH is below ∼1000 m, as PBLH increases, chemical production increases owing to the light enhancement whereas the ozone contribution from vertical mixing remains weak due to the decreased ozone concentration gradient between the surface and the upper layer, resulting in a net gain of surface ozone concentration. When the PBL continues to rise, chemical production contributes less mainly due to the decrease of ozone precursors at the surface, whereas vertical mixing becomes more vigorous that further diluting surface ozone, resulting in a net loss of surface ozone concentration, especially when PBLH reaches above ∼2000 m. Our results contribute to understanding the variability of surface ozone in Beijing area and provide insights for local ozone air quality control.

A 16-year climatology of the link between dry air intrusions and large-scale dust storms in North Africa

Understanding the underlying processes causing large-scale dust storms and their transport in North Africa is essential for their accurate prediction. Different atmospheric mechanisms have been identified to govern the emission of dust and its transport, ranging in scale and season, from Rossby wave breaking to local, low-level cold and dry jets. Connecting these features in a Lagrangian sense is a coherent airflow from the midlatitude upper troposphere to the surface where dust concentrations peak. Such dry intrusions (DIs) are linked to reduced static stability and strong, dry winds and indeed have been recently shown to play a central role in four of the largest dust storm in the region. Yet, the climatological link between DIs and dust storms over a long time period has not been studied yet. The aim of this paper is to identify objectively dust events that co-occur with DIs, and understand their spatio-temporal characteristics and underlying dynamical drivers. Combining Lagrangian-based DI identification in ERA-Interim and dust optical depth data in CAMS reanalyses, 325 events during 2003–2018 are identified. The events occur mostly in late winter and spring, when they are also larger in size and last longer, compared to summer events. When occurring with DIs, dust optical depth is generally higher, compared to events that are not accompanied by DIs. We focus on March events, and find coherent large-scale precursors and atmospheric conditions of dust-DI events: a northward jet shift over the North Atlantic with anticyclonic Rossby wave breaking occurs on average 4–5 days prior to the events. The lower troposphere responds with dry and cold conditions in northwest Africa, coinciding with the outflow of the DI airstream that is initially guided by the upper trough. Finally, elevated near-surface dust concentrations prevail on the leading edge of the DI in tropical west Africa, and in northeast Africa, where dust is exported to the Mediterranean in association with a Mediterranean cyclone.

Improved Gaussian plume model for atmospheric dispersion considering buoyancy and gravitational deposition: The case of multi-form tritium

Various types of radionuclides have different atmospheric dispersion characteristics, such as buoyancy and gravitational deposition phenomenon of light gas and heavy particles, respectively. Gaussian plume model was widely used to describe atmospheric dispersion behaviors of radioactive effluents, particularly for the purpose of engineering environmental impact assessment or nuclear emergency support. Nonetheless, buoyancy and gravitational deposition were rarely reported in previous work for tritium in particular, which might cause a deviation in evaluating near-surface concentration distribution and radiation dose to the public. Based on the multi-form tritium case, we made a quantitative description for the buoyancy and gravitational deposition phenomenon and discussed the feasibility of developing an improved Gaussian plume model to predict near-surface concentration distribution. Firstly, tritium concentration distribution near to the surface was predicted by using computational fluid dynamics method (CFD) and standard Gaussian plume model to reach consistency without consideration of buoyancy and gravitational deposition effects. Secondly, effects of buoyancy and gravitational deposition were identified by species transport model for gaseous tritium and discrete phase model for droplet tritium with integrating the buoyancy force caused by density variation of gaseous tritium and gravitational force of droplet tritium with enough size. Thirdly, buoyancy and gravitational deposition correction factors were obtained to modify the standard Gaussian plume model. Lastly, predictive results by improved Gaussian plume model were compared with CFD method. It was proved the improved correction method enables higher accuracy in predicting the atmospheric concentration distribution of gaseous pollutants with density variation or particles with gravitational deposition properties.