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The iron and steel industry is an important foundation of the national economy. It is the inevitable choice, to achieve high-quality development in the new era of the iron and steel industry, to speed up the green development of the iron and steel industry. This paper studies the effect of steel industry agglomeration on regional economic growth and air pollution. Through the analysis of the characteristics of iron and steel industry agglomeration, and the empirical analysis of the relationship between iron and steel industry agglomeration, regional economic growth, and air pollution, it is found that: (1) Iron and steel industry agglomeration helps to promote economic growth; (2) Iron and steel industry agglomeration has an obvious spatial correlation effect and obviously drives the development of surrounding areas; (3) Iron and steel industry agglomeration will cause air pollution. The marginal effect of air pollution will decline rapidly with the development of iron and steel industry agglomeration. (4) The impact of green process innovation investment on air pollution presents an inverted U-shaped effect, which has a positive effect on air recovery after exceeding the critical point. (5) The air self-purification capacity represented by precipitation, helps to reduce air pollution. Based on the above conclusions, this paper puts forward some policy suggestions, such as making a scientific development plan for the iron and steel industry, accelerating green process innovation, effectively improving regional precipitation and precipitation times, vigorously promoting high-quality development of the regional economy, and comprehensively promoting coordinated development of the iron and steel industry, so as to cope with the dilemma of the coordinated development of the iron and steel industry, regional economic growth, and smog pollution, and strive for international development in the future. In the competition, we should gain the first opportunity and obvious competitive advantage.

In this paper, a typical vortex system based on quasi-linear thermal-dynamic equations to reflect the development and extinction of the China Southwest Vortex is established using the vortex motion stability method combined with the outer environmental field and cumulus convective latent heat release. The development and extinction of the China Southwest Vortex in catastrophic weather systems are studied from the aspects of stability and development mechanisms for the primary-stage China Southwest Vortex, the transition mechanism from the primary-stage China Southwest Vortex to the mature vortex, and stability and development mechanisms of the mature China Southwest Vortex. The results show the following: (1) the convergence and divergence of the surrounding flow field is the main factor influencing the development and extinction of the primary-stage China Southwest Vortex, while gravity wave disturbance is the main driving force for the maintenance and development of the primary vortex. Based on the convergence of the external flow field, the gravity wave disturbance must exceed the critical frequency, or the vortex will tend to die out. (2) The convergence and divergence of the surrounding flow field is also the main factor for the transition from the primary vortex to the mature vortex. Based on the convergence of the surrounding flow field, the primary vortex transforms into a mature vortex only when the gravity wave disturbance strongly exceeds the critical frequency and causes the vertical disturbance to become unstable. (3) The convergence and divergence of the external flow field is also the main factor for the development and extinction of the mature China Southwest Vortex. In the early stage, the vortex can be maintained and developed as long as the surrounding flow field converges. In the case of the divergence of the external flow field, the vortex may be maintained for a short time, but eventually dissipates when the gravity wave disturbance exceeds the critical frequency. In the later stage, under the convergence of the surrounding flow field, the vortex can be maintained when the gravity wave disturbance exceeds the critical frequency. However, with the divergence of the surrounding flow field, the vortex may be maintained for a short time, but it will eventually dissipate when the gravity wave disturbance is extremely strong. In addition, the observations of the evolution of China Southwest Vortexes and gravity wave activities under the influence of southwest airflow and atmospheric disturbance in the Western Sichuan Plateau–Sichuan Basin are explained by the above physical mechanism. It is also pointed out that the heating effect can be an obstacle to the development of the China Southwest Vortex by increasing the critical frequency of gravity waves during unstable layer formation, and the divergent environment flow field under the condition of stable layer formation. Therefore, this paper deepens the understanding of the evolution process and anomalous mechanisms of the China Southwest Vortex.

There is currently no way for subway air-conditioning systems to adjust to changes in passenger capacity in terms of air-supply volume and air-supply temperature. In this paper, a study of the setting of the air-supply volume and controlling the temperature in subway trains was conducted. Based on the fact that the air-supply volume in a subway train needs to meet the requirements of cooling load and humidity load, as well as fresh-air volume, the minimum critical air-supply volume curve was determined on the basis of theoretical calculations under different passenger capacities. Under different passenger capacity and air-supply-volume conditions, the PMV (predicted mean vote) distribution of the subway train was simulated and calculated, and the results indicated that increasing the air-supply volume would not be sufficient to achieve PMV = 0. Therefore, in accordance with the theoretical formulation of the PMV, multiple linear regression was used to fit the temperature-control curve for various passenger capacities. A simulation calculation of the subway train was used to assess the uniformity of the flow field and the temperature field within the subway train in accordance with EN14750. It is evident from the results that under the control curve of the air-supply volume and temperature under different passenger capacities, the uniformity of the flow field and the temperature field can meet the standard requirements. By using the air-supply volume and temperature-control curve proposed in this paper, it is possible to adjust the air-supply volume in real-time, control the temperature of the air-conditioning system of the subway train, improve the passenger comfort of the train, and reduce the energy consumption of the air-conditioning system.

The Joint Polar Satellite System (JPSS) mission has provided over ten years of high-quality data products for environment forecasting and monitoring through the current Suomi National Polar-orbiting Partnership (S-NPP) and NOAA-20 satellites. Particularly, the sensor data record (SDR) and the derived environmental data record (EDR) products from the Visible Infrared Imaging Radiometer Suite (VIIRS), the Cross-track Infrared Sounder (CrIS), the Advanced Technology Microwave Sounder (ATMS), and the Ozone Mapping and Profiler Suite (OMPS) offer an unprecedented opportunity to observe severe weather and environmental events over the Earth. This paper presents the observations about atmospheric features of the Hunga Tonga Volcanic eruption of January 2022, e.g., the gravity wave, volcanic cloud, and aerosol (sulfate) plume phenomena, by using the ATMS, CrIS, OMPS, and VIIRS SDR and EDR products. Powerful gravity waves ringing through the atmosphere after the eruption of the Hunga Tonga volcano are discovered at two CrIS upper sounding channels (670 cm−1 and 2320 cm−1) in the deviations of the observed brightness temperature (O) from the simulated baseline brightness temperature (B) using the Community Radiative Transfer Model (CRTM), i.e., O—B. A similar pattern is also observed in the ATMS global maps at channel 15, whose peak weighting function is around 40 km, showing the atmospheric disturbance caused by the eruption that reached 40 km above the surface. The Tonga volcanic cloud (plume) was also captured by the OMPS SO2 EDR product. The gravity wave features were also captured in the native resolution image of the S-NPP VIIRS I-5 band nighttime observations. In addition, the VIIRS Aerosol Optical Depth (AOD) captured and tracked the volcanic aerosol (sulfate) plume successfully. These discoveries demonstrate the scientific potential of the JPSS SDR and EDR products in monitoring and tracking the eruption of the Hunga Tonga volcano and its severe environmental impacts. This paper presents the atmospheric features of the Hunga Tonga volcano eruption that is uniquely captured by all four advanced sensors onboard JPSS satellites, with different spectral coverages and spatial resolutions.

The rescaling of rainfall requires measurements of rainfall rates over many dimensions. This paper develops one approach using 10 m vertical spatial observations of the Doppler spectra of falling rain every 10 s over intervals varying from 15 up to 41 min in two different locations and in two different years using two different micro-rain radars (MRR). The transformation of the temporal domain into spatial observations uses the Taylor “frozen” turbulence hypothesis to estimate an average advection speed over an entire observation interval. Thus, when no other advection estimates are possible, this paper offers a new approach for estimating the appropriate frozen turbulence advection speed by minimizing power spectral differences between the ensemble of purely spatial radial power spectra observed at all times in the vertical and those using the ensemble of temporal spectra at all heights to yield statistically reliable scaling relations. Thus, it is likely that MRR and other vertically pointing Doppler radars may often help to obviate the need for expensive and immobile large networks of instruments in order to determine such scaling relations but not the need of those radars for surveillance.

This paper investigates the effect of the architectural design of deep learning models in combination with a feature engineering approach considering the temporal variation in the features in the case of tropospheric ozone forecasting. Although deep neural network models have shown successful results by extracting features automatically from raw data, their performance in the domain of air quality forecasting is influenced by different feature analysis approaches and model architectures. This paper proposes a simple but effective analysis of tropospheric ozone time series data that can reveal temporal phases of the ozone evolution process and assist neural network models to reflect these temporal variations. We demonstrate that addressing the ozone evolution phases when developing the model architecture improves the performance of deep neural network models. As a result, we evaluated our approach on the CNN model and showed that not only does it improve the performance of the CNN model, but also that the CNN model in combination with our approach boosts the performance of the other deep neural network models such as LSTM. The development of the CNN, LSTM-CNN, and CNN-LSTM models using the proposed approach improved the prediction performance of the models by 3.58%, 1.68%, and 3.37%, respectively.

Ozone is a very important trace gas in the stratosphere and, thus, we need to know its time evolution over the globe. However, ground-based measurements are rare, especially in the Southern Hemisphere, and while satellite observations provide broader spatial coverage generally, they are not available everywhere. On the other hand, reanalysis data have regular spatial and temporal structure, which is beneficial for trend analysis, but temporal discontinuities might exist in the data. These discontinuities may influence the results of trend studies. The aim of this paper is to detect discontinuities in ozone data of the following reanalyses: MERRA-2, ERA-5 and JRA-55 with the help of the Pettitt, the Buishand, and the Standard Normal Homogeneity tests above the 500 hPa level. The share of discontinuities varies from 30% to 70% and they are strongly layer dependent. The share of discontinuities is the lowest for JRA-55. Differences between reanalyses were found to be larger than differences between homogeneity tests within one reanalysis. Another aim of this paper is to test the ability of homogeneity tests to detect the discontinuities in 2004 and 2015, when changes in versions of satellite data took place. We showed the discontinuities in 2004 are better detected than those in 2015.

PM2.5 is one of the main pollutants that cause air pollution, and high concentrations of PM2.5 seriously threaten human health. Therefore, an accurate prediction of PM2.5 concentration has great practical significance for air quality detection, air pollution restoration, and human health. This paper uses the historical air quality concentration data and meteorological data of the Beijing Olympic Sports Center as the research object. This paper establishes a long short-term memory (LSTM) model with a time window size of 12, establishes a T-shape light gradient boosting machine (TSLightGBM) model that uses all information in the time window as the next period of prediction input, and establishes a LSTM-TSLightGBM model pair based on an optimal weighted combination method. PM2.5 hourly concentration is predicted. The prediction results on the test set show that the mean squared error (MAE), root mean squared error (RMSE), and symmetric mean absolute percentage error (SMAPE) of the LSTM-TSLightGBM model are 11.873, 22.516, and 19.540%, respectively. Compared with LSTM, TSLightGBM, the recurrent neural network (RNN), and other models, the LSTM-TSLightGBM model has a lower MAE, RMSE, and SMAPE, and higher prediction accuracy for PM2.5 and better goodness-of-fit.

This paper focuses on a detailed comparison, based on the F2-layer peak characteristics foF2 and hmF2, between the International Reference Ionosphere (IRI), which is a climatological empirical model of the terrestrial ionosphere, and the IRI Real-Time Assimilative Mapping (IRTAM) procedure, which is a real-time version of IRI based on data assimilation from a global network of ionosondes. To perform such a comparison, two different kinds of datasets have been considered: (1) foF2 and hmF2 as recorded by 40 ground-based ionosondes spread all over the world from 2000 to 2019, (2) foF2 and hmF2 from space-based COSMIC/FORMOSAT-3 radio occultation measurements recorded from 2006 to 2018. The aim of the paper is to understand whether and how much IRTAM improves IRI foF2 and hmF2 outputs for different locations and under different diurnal, seasonal, solar and magnetic activity conditions. The main outcomes of the study are: (1) when ionosonde observations are considered for validation, IRTAM significantly improves the IRI foF2 modeling both in accuracy and precision, while a slight improvement in the IRI hmF2 modeling is observed for specific locations and conditions, (2) when COSMIC observations are considered for validation, no noticeable improvement is observed from the IRTAM side for both foF2 and hmF2. Indeed, IRTAM can improve the IRI foF2 description only nearby the assimilated ionosonde locations, while the IRI hmF2 description is always more accurate and precise than IRTAM one.

Estimation of Meteorological visibility from image characteristics is a challenging problem in the research of meteorological parameters estimation. Meteorological visibility can be used to indicate the weather transparency and this indicator is important for transport safety. This paper summarizes the outcomes of the experimental evaluation of a Particle Swarm Optimization (PSO) based transfer learning method for meteorological visibility estimation method. This paper proposes a modified approach of the transfer learning method for visibility estimation by using PSO feature selection. Image data are collected at fixed location with fixed viewing angle. The database images were gone through a pre-processing step of gray-averaging so as to provide information of static landmark objects for automatic extraction of effective regions from images. Effective regions are then extracted from image database and the image features are then extracted from the Neural Network. Subset of Image features are selected based on the Particle Swarming Optimization (PSO) methods to obtain the image feature vectors for each effective sub-region. The image feature vectors are then used to estimate the visibilities of the images by using the Multiple Support Vector Regression (SVR) models. Experimental results show that the proposed method can give an accuracy more than 90% for visibility estimation and the proposed method is effective and robust.

This paper extends the work of previous research by investigating surfing practices and surf-recreation companies from a behavioral perspective. The study’s main aim is to gain insights into the role of meteorological/climatological information in decision-making related to the surf-tourism activities market. This information was gathered employing an online survey that asked respondents about where they surf and how they check forecasts for surfing. Climate services (CS) are promoted to support the decision-making process to better prepare for and adapt to the risks and opportunities of climate variability and change. The current market for CS is still in its early stages. In this paper, we report the findings from our recent investigation into the actual and potential market for CS for the Iberian Peninsula surf-tourism sector. Based on surfers’ and surf companies’ demands, it was found that an improved surfing climate service (herein, SCS) will have clear implications in the management of these tourism areas and provide insights into whether surfing activities may be successful. At the same time, such services can help to manage adaptive actions in regard to the impacts of climate change in surfing areas.

Emissions of fluorinated greenhouse gases (F-gases), which are used as replacements for ozone-depleting substances, have risen sharply since 1995. The rapid increase in F-gas emissions coupled with their global warming potential (GWP) has led to increased worldwide attention to monitoring emission levels and subsequently regulating the use of F-gases. These restrictions apply in particular to applications for which alternative technologies are available that are more economically efficient and have minor or no impact on the Earth’s climate system. This paper brings new information about changes in composition of consumed F-gases in the Czech Republic. Since no F-gases are produced in the country, data about F-gas consumption are obtained from three resources which give information about import and export. The paper also describes implementation of newly used F-gases, which are used as replacements for specific F-gases, into emission calculation models. Emissions are estimated according to the methodology developed by the Intergovernmental Panel on Climate Change (IPCC). Although consumption of F-gases with high GWP has already started decreasing, it will have no effect on F-gas emissions for several years.

The North China Famine of 1876&ndash, 1879, known in Chinese as the Dingwu qihuang (丁戊奇荒), is a famous case of drought-induced famine in Chinese history. The purpose of this paper is to provide empirical and historical evidence for understanding the impacts of extreme climate events and major disasters and the mechanisms of adaptation. From the aspects of famine-related migration and the allocation of relief money and grain, the regional interactions in social responses to extreme climate events were analyzed. This paper collected 186 records from historical documents. Regarding the regions as the nodes and the relationships between regions as the links, the spatial patterns of famine-related migration and the allocation of money and grain from 1877 to 1878 were rebuilt. The results show that, firstly, famine-related migration appeared to be spontaneous and short-distanced, with the flow mainly spreading to the surrounding areas and towns. Secondly, as a state administrative action, the relief money and grain from the non-disaster areas were distributed to the disaster areas. However, the distribution of relief grain affected the equilibrium of the food market in non-disaster areas, which led to fluctuations in food prices.

This paper describes the density correction of the NRLMSISE-00 using more than 15 years (2002&ndash, 2016) of TIMED/SABER satellite atmospheric density data from the middle atmosphere (20&ndash, 100 km). A bias correction factor dataset is established based on the density differences between the TIMED/SABER data and NRLMSISE-00. Seven height nodes are set in the range between 20 and 100 km. The different scale oscillations of the correction factor are separated at each height node, and the spherical harmonic function is used to fit the coefficients of the different timescale oscillations to obtain a spatiotemporal function at each height node. Cubic spline interpolation is used to obtain the correction factor at other non-node heights. The spatiotemporal correction function depends on six key parameters, including height, latitude, longitude, local time, day, and year. The evaluation results show that the spatiotemporal correction function proposed in this paper achieves a good correction effect on the atmospheric density of NRLMSISE-00. The correction effect becomes more pronounced as the height increases. After correction, the relative error of the model decreased by 40%&ndash, 50% in July, especially at &plusmn, 40&deg, N in the 80&ndash, 100 km region. The correction effect of the spatiotemporal correction function under different geomagnetic activity may have some potential relationships with geomagnetic activities. During geomagnetic storms, the relative errors in atmospheric density at 100, 70, and 32 km decrease from 41.21%, 22.09%, and 3.03% to &minus, 9.65%, 2.60%, and 1.44%, respectively, after correction. The relative errors in atmospheric density at 100, 70, and 32 km decrease from 68.95%, 21.02%, and 3.56% to 3.49%, 2.20%, and 1.77%, respectively, during the geomagnetic quiet period. The correction effect during the geomagnetic quiet period is better than that during geomagnetic storms at a height of 100 km. The subsequent effects of geomagnetic activity will be considered, and the atmospheric density during magnetic storms and quiet periods will be corrected separately near 100 km. The ability of the model to characterize the mid-atmosphere (20&ndash, 100 km) is significantly improved compared with the pre-correction performance. As a result, the corrected NRLMSISE-00 can provide more reliable atmospheric density data for scientific researches and engineering fields, such as data analysis, instrument design, and aerospace vehicles.

This paper investigates the literature and theoretical underpinning of the concept of &ldquo, willingness to pay&rdquo, (WTP) for green infrastructure (GI) with consequences for residential development. The benefits of GI in urban settlements include improved air quality, attenuation of the urban heat island, thermal insulation and lower energy bills for green roofs and walls, the creation of social amenity space, a habitat for biodiversity, and stormwater water attenuation. Collectively, these benefits are termed eco-system services and enhance sustainability. The role of GI, the &ldquo, lungs of the planet&rdquo, is heavily correlated to atmospheric conditions, high levels of GI improve air quality, which is acknowledged widely with many cities increasing GI to make them more resilient to future predicted challenges with respect to heat and poor air quality. In addition, there is evidence that the biophilia effect enhances human well-being. There are some studies claiming that purchasers pay a premium for property with good GI. However, there is little research about the process in consumers&rsquo, minds leading to such a premium&mdash, if, how, when (under what circumstances), and then to what extent are consumers willing to pay for GI. This process, if better understood, may enable sellers or policy makers to influence the amounts of GI in developments, thus making it possible to enhance the value of GI to buyers. There is some research pointing to factors to be considered when modeling such processes. For developers, knowing the optimum amount of GI would enable them to design and construct developments with maximum purchaser appeal. To do this, stakeholders need to predict the level of WTP amongst potential purchasers for which they need to understand the decision processes behind WTP. In this way, sustainability in residential property development could be optimized. The paper analyzes the literature and theories concerning WTP, focusing on dwellings and GI. Our findings are that some quantitative evidence exists that purchasers pay more for residential property with high levels of GI in some cities, but they do so without any understanding of the possible decision processes leading to those premiums (if, how, when, and then to what extent). The paper proposes a comprehensive conceptual model that may explain buyers&rsquo, WTP for a dwelling based on a presumed cost&ndash, benefit analysis performed by buyers, which has been extended here to include GI and psychological factors. Thus, the paper has a consumer perspective. The model may be used to select variables and test them in empirical studies, and by integrating with other factors in the model, it can attain a more comprehensive understanding of WTP for GI in residential development.

Atmospheric emissions of atomic and molecular oxygen have been observed since the middle of 19th century. In the last decades, it has been shown that emissions of excited oxygen atom O(1D) and molecular oxygen in electronically&ndash, vibrationally excited states O2(b1&Sigma, +g, v) and O2(a1&Delta, g, v) are related by a unified photochemical mechanism in the mesosphere and lower thermosphere (MLT). The current paper consists of two parts: a review of studies related to the development of the model of ozone and molecular oxygen photodissociation in the daytime MLT and new results. In particular, the paper includes a detailed description of formation mechanism for excited oxygen components in the daytime MLT and presents comparison of widely used photochemical models. The paper also demonstrates new results such as new suggestions about possible products for collisional reactions of electronically&ndash, vibrationally excited oxygen molecules with atomic oxygen and new estimations of O2(b1&Sigma, +g, v = 0&ndash, 10) radiative lifetimes which are necessary for solving inverse problems in the lower thermosphere. Moreover, special attention is given to the &ldquo, Barth&rsquo, s mechanism&rdquo, in order to demonstrate that for different sets of fitting coefficients its contribution to O2(b1&Sigma, g, v) population is neglectable in daytime conditions. In addition to the review and new results, possible applications of the daytime oxygen emissions are presented, e.g., the altitude profiles O(3P), O3 and CO2 can be retrieved by solving inverse photochemical problems when emissions from electronically vibrationally excited states of O2 molecule are used as proxies.

Considering the wide range of variability of all aerosol characteristics (especially in the near-ground layer of the atmosphere near industrial centers), when creating a realistic empirical model of optical and microphysical characteristics, the optimal dividing of the total data array according to some multifactor signs is needed. In this paper, we analyze the main states of &ldquo, dry&rdquo, aerosol on the basis of the results of long-term regular measurements in the near-ground layer of the atmosphere near the city of Tomsk in 2000&ndash, 2017. The following parameters were considered: aerosol number concentration and size distribution function, total and angular scattering coefficients, including the small-angle range 1.2&deg, to 20&deg, mass concentration and size distribution of absorbing substances (equivalent black carbon), characteristics of the aerosol hygroscopic properties, and spectral aerosol extinction of radiation on an open long path in the wavelength range 0.45 to 3.9 &micro, m. In our comprehensive study, we first proposed and developed an original approach (classification) to study the optical and microphysical properties of atmospheric aerosol of various physicochemical origins (background, smoke, smog, anthropogenic, etc.) based on dividing the entire data array into characteristic subarrays (types of aerosol weather), which differ from each other in a different combination of scattering and absorbing properties of particles. To divide the total data array into types of aerosol weather including &ldquo, Background&rdquo, &ldquo, Haze-S&rdquo, Smog&rdquo, and &ldquo, Smoke haze&rdquo, the values of the scattering coefficient of the dry aerosol matter &sigma, d(&lambda, = 0.51 &mu, m) = 100 Mm&minus, 1 and the ratio of the mass concentration of the absorbing substance to the mass concentration of submicron aerosol P = 0.05. The results showed that most of the seasonal average values of the aerosol parameters analyzed in the paper are statistically significantly different when comparing various characteristic types of scattering and absorbing atmospheric aerosol. The results of the research indicate that the application of the developed classification of types of aerosol weather for the analyzed optical and microphysical parameters of aerosol particles is quite effective and reasonable.

Through the observation of ground-based LIDAR and satellite sensors, the weather conditions of continuous foggy-hazy alternations in the Huainan region from 26 December 2016 to 5 January 2017 were analyzed and observed. In this study, the formation and influence of this event were discussed by analyzing pollutant concentrations, meteorological factors and aerosol optical characteristics. The concentrations of PM10 and PM2.5 increased significantly. The maximum value of PM10 was 412 &mu, g/m3, and the maximum value of PM2.5 was 258 &mu, g/m3. The transportation of pollutants and the production of man-made pollutants promote the accumulation of pollutants. In this weather process, meteorological factors such as the surface wind speed, humidity, surface temperature, and inversion also promote the accumulation of pollutants, which is the main reason for the formation of this weather process. Furthermore, the near surface air mass mainly came from the cities near the Huainan region and the heavily polluted areas in the north, while the upper air mass came from Inner Mongolia. In this paper, piecewise inversion was adopted to achieve accurate all-weather extinction coefficient profile inversion by reasonably selecting a cloud LIDAR ratio through a backscatter ratio, and the LIDAR ratio of cloud in this period was 22.57&ndash, 34.14 Sr. By means of extinction coefficient inversion and correlation analysis, the correlation index of PM2.5 and the aerosol optical depth (AOD) was 0.7368, indicating that there was a positive correlation between PM2.5 and AOD, and AOD can also reflect the pollution condition of this region. The formation process of foggy-hazy weather in the Huainan region studied in this paper can provide a research basis for foggy-hazy pollution in this region.

Based on the 19 year observation from 1998 to 2016 at the Tsuan Wan and Central/Western District monitoring stations in Hong Kong, the aim of this paper was to assess the wet deposition pathway of Benzo(a)pyrene (BaP) on a large time-scale. In order to achieve this goal, multi-fractal detrended cross-correlation analysis (MF-DCCA) was used to characterize the long-term cross-correlations behaviors and multi-fractal temporal scaling properties between BaP (or PM2.5) and precipitation. The results showed that the relationships between BaP and precipitation (or PM2.5) displayed long-term cross-correlation at the time-scale ranging from one month to one year, no cross-correlation between each other was observed in longer temporal scaling regimes (greater than one year). These results correspond to the atmospheric circulation of the Asian monsoon system and are explained in detail. Similar dynamic processes of the wet deposition of BaP and PM2.5 suggested that the main removal process of atmospheric BaP was rainfall deposits of PM2.5-bound BaP. Furthermore, cross-correlations between BaP (or PM2.5) and precipitation at the long time-scale have a multi-fractal nature and long-term persistent power-law decaying behavior. The temporal evolutions of the multi-fractality were investigated by the approach of a sliding window. Based on the evolution curves of multi-fractal parameters, the wet deposition pathway of PM2.5-bound BaP is discussed. Finally, the contribution degree of wet deposition to PM2.5-bound BaP was derived from the coefficient of determination. It was demonstrated that about 45% and 60% of atmospheric BaP removal can be attributed to the wet deposition pathway of PM2.5-bound BaP for the Tsuan Wan and Central/Western District areas, respectively. The findings in this paper are of great significance for further study on the removal mechanism of atmospheric BaP in the future. The MF-DCCA method provides a novel approach to assessing the geochemical cycle dynamics of BaP.

The plum rain season, caused by precipitation along a persistent stationary Mei-Yu front in East Asia, creates favorable temperatures and relative humidity (RH) for mold growth indoors. This paper investigates the effects of human occupancy on indoor humidity and investigates the efficient RH reduction methods to prevent mold growth in moist climates. The research is carried out based on a case study which compares a family-occupied home and another unoccupied one during typical plum rain season in Nanjing. Firstly, by analyzing the factors that can influence the indoor air RH, this paper develops a comprehensive model to evaluate the efficiency of various RH intervention methods. Secondly, this paper collects the meteorological data in Nanjing at different time scales, from days to hours. Thirdly, a specific case study is carried out based on the model and data. The results show that dehumidification and heating can always reduce RH below the critical value under which the mold growth could be inhibited. However, the effects of ventilation are more sophisticated and depend upon the human occupancy, outdoor air temperature, and air change per hour (ACH). In certain unoccupied cases, the ventilation may be inappropriate and may continuously bring moisture outside into the indoor environment, which has adverse effects on mold suppression. In the occupied cases, the condition changes significantly because the human is deemed as an internal source of heat and moist. Special care should be exercised for occupied ventilation in order to determine the optimal ACH and appropriate outdoor temperatures. Finally, some guidance is given to prevent mold growth in the general area that suffers from the plum rain season.

As a main grain production area in China, Northeast China (NEC) is highly influenced by the higher warming trend than elsewhere in China or even the globe. As the warming trend goes on, scientific understanding of the changes of thermal conditions in NEC will be essential for taking agricultural adaptation measures. In this paper, the high-resolution (25 km) corrected outputs of PRECIS (Providing Regional Climates for Impacts Studies) under the Representative Concentration Pathway 4.5 (RCP4.5) scenario were used to analyze the changes of the agro-climatic thermal resources in NEC. Results showed that accumulated temperature &ge, 10 &deg, C (AT10) could increase by 300&ndash, 500 &deg, C&middot, day, 500&ndash, 900 &deg, day, and 750&ndash, 1000 &deg, day in 2011&ndash, 2040 (2020s), 2041&ndash, 2070 (2050s), and 2071&ndash, 2100 (2080s), respectively, relative to the baseline period of 1961&ndash, 1990. The potential growing season (PGS) would then be prolonged by 11&ndash, 19 days, 23&ndash, 35 days, and 25&ndash, 37 days in 2020s, 2050s and 2080s, respectively, compared to baseline. The spatial features for the changes of thermal indices would show a large increase of AT10 and an extension of PGS and Frost-free period (FFP) in mountainous areas compared to plains, the increment of AT10 would be greater in southern NEC than that in the north, with over 1000 &deg, day in the southern parts and 700&ndash, 800 &deg, day in the northern parts in the 2080s. Additionally, the three thermal indices would increase rapidly from 2020s to 2050s compared to the period from 2050s to the 2080s, coincides with the greenhouse gas concentrations peak around the mid-period of the 21st century, and the decline towards the end of the 21st century under RCP4.5 forcing. The results of this paper could act as a guide to taking advantage of increasing thermal resources in NEC, and would be helpful for local practitioners in decision-making.

This paper discusses results of the CLOUD-MAP (Collaboration Leading Operational UAS Development for Meteorology and Atmospheric Physics) project dedicated to developing, fielding, and evaluating integrated small unmanned aircraft systems (sUAS) for enhanced atmospheric physics measurements. The project team includes atmospheric scientists, meteorologists, engineers, computer scientists, geographers, and chemists necessary to evaluate the needs and develop the advanced sensing and imaging, robust autonomous navigation, enhanced data communication, and data management capabilities required to use sUAS in atmospheric physics. Annual integrated evaluation of the systems in coordinated field tests are being used to validate sensor performance while integrated into various sUAS platforms. This paper focuses on aspects related to atmospheric sampling of thermodynamic parameters with sUAS, specifically sensor integration and calibration/validation, particularly as it relates to boundary layer profiling. Validation of sensor output is performed by comparing measurements with known values, including instrumented towers, radiosondes, and other validated sUAS platforms. Experiments to determine the impact of sensor location and vehicle operation have been performed, with sensor aspiration a major factor. Measurements are robust provided that instrument packages are properly mounted in locations that provide adequate air flow and proper solar shielding.

In this paper we suggest Penning ionization as an important route of formation for ionic species in upper planetary atmospheres. Our goal is to provide relevant tools to researchers working on kinetic models of atmospheric interest, in order to include Penning ionizations in their calculations as fast processes promoting reactions that cannot be neglected. Ions are extremely important for the transmission of radio and satellite signals, and they govern the chemistry of planetary ionospheres. Molecular ions have also been detected in comet tails. In this paper recent experimental results concerning production of simple ionic species of atmospheric interest are presented and discussed. Such results concern the formation of free ions in collisional ionization of H2O, H2S, and NH3 induced by highly excited species (Penning ionization) as metastable noble gas atoms. The effect of Penning ionization still has not been considered in the modeling of terrestrial and extraterrestrial objects so far, even, though metastable helium is formed by radiative recombination of He+ ions with electrons. Because helium is the second most abundant element of the universe, Penning ionization of atomic or molecular species by He*(23S1) is plausibly an active route of ionization in relatively dense environments exposed to cosmic rays.

An optimum operation of a gasoline particulate filter (GPF) for a gasoline direct injection vehicle in terms of its performance of pressure drop, soot loading, and filtration efficiency becomes inevitable to fulfill upcoming emission regulations. This paper proposes a methodology to characterize the GPF performance for validation of simulation models for more precise operation strategies along with future legislative requirements. The feasibility was examined through experiments of miniature GPF samples using a synthetic particle generator. Firstly, permeability of the GPF walls was estimated to be 6.9 ± 1.5 × 10−13 m2 by a flow resistance descriptor model, which was in good agreement with its pore structure. Secondly, photoacoustic sensor systems indicated soot accumulation inside the GPFs in real time thanks to linear correlations between sensor signals and soot mass concentrations in exhaust. Thirdly, particle number counting systems compliant with respective regulatory technical requirements exhibited time-resolved filtration efficiencies of the GPFs in conjunction with solid particle number emissions whose diameter was larger than 10 nm and 23 nm. The filtration efficiencies at a clean state of the GPF were 0.78 and 0.77, respectively. The slight difference could be explained by Brownian diffusion and interception for particle filtration.

Frequency analysis of tornadoes is very important for risk analysis and disaster control. In this paper, the annual frequency of tornadoes that occurred in the United States from 1967 to 2016 is analyzed. The simple analysis shows that frequencies of tornadoes of different sites are spatially correlated and over-dispersed. To explain the two characteristics of the data, the Bayesian hierarchical model is proposed. For comparison purposes, the Bayesian model with negative binomial distribution, Poisson distribution, Polya distribution, and first-order, non-negative, integer-valued autoregressive model with Bell innovations(BL-INAR(1)) are considered to fit the frequency of tornado. The distribution parameters of all sites are assumed to be spatially correlated, and the corresponding Bayesian hierarchical models were established. MCMC (Markov Chain Monte Carlo) method is applied to parameter estimations and relative statistical inference. By comparison of the analysis results, the negative binomial distribution is recommended to analyze the overdispersion and spatial correlation among the sites of the data. The comparison between the simulated frequencies based on the proposed model and the actual frequencies also verifies that the proposed method is a better model for the data.

International audience; This paper reports two experimental fires conducted at field-scale in Corsica, across a particular mountain shrubland. The orientation of the experimental plots was chosen in such a way that the wind was aligned along the main slope direction in order to obtain a high intensity fire. The first objective was to study the high intensity fire behavior by evaluating the propagation conditions related to its speed and intensity, as well as the geometry of the fire front and its impact on different targets. Therefore, an experimental protocol was designed to determine the properties of the fire spread using UAV cameras and its impact using heat flux gauges. Another objective was to study these experiments numerically using a fully physical fire model, namely FireStar3D. Numerical results concerning the fire dynamics, particularly the ROS, were also compared to other predictions of the FireStar2D model. The comparison with experimental measurements showed the robustness of the 3D approach with a maximum difference of 5.2% for the head fire ROS. The fire intensities obtained revealed that these experiments are representative of high intensity fires, which are very difficult to control in the case of real wildfires. Other parameters investigated numerically (flame geometry and heat fluxes) were also in fairly good agreement with the experimental measurements and confirm the capacity of FireStar3D to predict surface fires of high intensity.

Nitrogen dioxide (NO2) is an important precursor of atmospheric aerosol. Forecasting urban NO2 concentration is vital for effective control of air pollution. This paper proposes a hybrid deep learning model for predicting daily average NO2 concentrations on the next day, based on atmospheric pollutants, meteorological data, and historical data during 2014 to 2020 in five coastal cities of Shandong peninsula, northern China. A random Forest (RF) algorithm was used to select input variables to reduce data dimensionality trained by the sequence to sequence (Seq2Seq) the model and describe how the Seq2Seq model understands each predictor variable. The hybrid model combining an RF with Seq2Seq network (RF-S2S) was evaluated and achieved a Pearson’s correlation coefficient of 0.93, a Nash–Sutcliffe coefficient (NS) of 0.79, a Root Mean Square Error (RMSE) of 5.85 µg/m3, a Mean Absolute Error (MAE) of 4.50 µg/m3, and a Mean Absolute Percentage Error (MAPE) of 20.86%. Feature selection by an RF model improves the performance of the Seq2Seq model, reducing errors by 19.7% (RMSE), 20.3% (MAE), and 29.3% (MAPE), respectively. Carbon monoxide (CO) and PM10 are two common, important features influencing the prediction of NO2 concentrations in coastal areas of northern China. The results of RF-S2S models can capture general trends and disruptions more accurately than can long-short term memory (LSTM) models with and without feature selection. The decreasing tendency of NO2 from 2014 to 2020 illustrated by the empirical mode decomposition (EMD) method is one important obstacle to improving the RF-S2S prediction accuracy. An EMD-based RF-S2S model could help to perform the short-term forecast of NO2 concentrations efficiently.

The main goal of this paper is to study pollution during sea breeze days in the Split town center, which is placed near the industrial area with three cement plants and one asbestos cement plant, as well as a harbor with high traffic, and investigate the sources of pollution with SO2 and its relation to atmospheric parameters using stepwise multiple linear regression (MLR). The hourly temperature difference from the time of the sea breeze lull (dT) was considered in evaluating the influence of meteorological parameters on hourly pollutant concentrations. It was found that the wind direction index (WDI) is a significant predictor for the sea breeze, and wind speed, relative humidity, and dT are significant for the land breeze. A very high index of agreement of 0.9 was obtained by the MLR model for the land breeze, and 0.8 for the sea breeze. Low SO2 concentrations are observed at night, and increased values are found between 0800 and 1800 UTC. With WDI being the only predictor during sea breeze, local traffic is found to be the main anthropogenic source of SO2 pollution.

This paper explored the changes of six significant pollutants (PM2.5, PM10, SO2, NO2, O3, and CO) in Jilin City during the coronavirus disease 2019 (COVID-19) epidemic in 2022, and compared them with the same period of previous years to analyze the impact of anthropogenic emissions on the concentration of pollutants; The Weather Research and Forecasting Community Multiscale Air Quality (WRF–CMAQ) model was used to evaluate the effect of meteorological factors on pollutant concentration. The results showed that except for O3, the concentrations of the other five pollutants decreased significantly, with a range of 21–47%, during the lockdown period caused by the government’s shutdown and travel restrictions. Compared with the same period in 2021, the decrease of PM2.5 was only 25% of PM10. That was because there was still a large amount of PM2.5 produced by coal-fired heating during the blockade period, which made the decrease of PM2.5 more minor. A heavy pollution event caused by adverse meteorological conditions was found during the lockdown period, indicating that only controlling artificial emissions cannot eliminate the occurrence of severe pollution events. The WRF–CMAQ results showed that the lower pollutant concentration in 2022 was not only caused by the reduction of anthropogenic emissions but also related to the influence of favorable meteorological factors (higher planetary boundary layer thickness, higher wind speed, and higher temperature).

Wind power has great potential in the fields of electricity generation, heating, et cetera, and the precise forecasting of wind speed has become the key task in an effort to improve the efficiency of wind energy development. Nowadays, many existing studies have investigated wind speed prediction, but they often simply preprocess raw data and also ignore the nonlinear features in the residual part, which should be given special treatment for more accurate forecasting. Meanwhile, the mainstream in this field is point prediction which cannot show the potential uncertainty of predicted values. Therefore, this paper develops a two-stage decomposition ensemble interval prediction model. The original wind speed series is firstly decomposed using a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the decomposed subseries with the highest approximate entropy is secondly decomposed through singular-spectrum analysis (SSA) to further reduce the complexity of the data. After two-stage decomposition, auto-encoder dimensionality reduction is employed to alleviate the accumulated error problem. Then, each reconstructed subsequence will generate an independent prediction result using an elastic neural network. Extreme gradient boosting (Xgboost) is utilized to integrate the separate predicted values and also carry out the error correction. Finally, the Gaussian process (GP) will generate the interval prediction result. The case study shows the best performance of the proposed models, not only in point prediction but also in interval prediction.

Methane (CH4) is the second-largest greenhouse gas emitted by human activity and natural sources after carbon dioxide (CO2). Its relatively short lifetime in the atmosphere (about 12 years) means that we can mitigate the human impacts of climate change in a relatively short period of time by reducing CH4 emissions. The creation of CH4 emissions management policies can be based on the distribution maps of surface CH4 concentration that are in large-scale and at high-resolution. The estimate of CH4 emissions with broad coverage are provided by currently extensively used satellite data supplemented with data from model simulations. However, it is at low spatial resolution. In this paper, through the combination of atmospheric CH4 observations from the TROPOMI sensor and wind data from the ECMWF global reanalysis, a straightforward divergence method is proposed to estimate the surface CH4 emissions in China from March 2019 to September 2022 at a resolution of 7 km × 7 km. This method was compared with the average annual CH4 emissions of Emissions Database for Global Atmospheric Research (EDGARv7.0), and the Root Mean Square Error (RMSE) is 2.53 kg/km2/h and within error envelop (EE) is 72.93%, which represents the proportion of reliable values under certain uncertain conditions. We estimated that the average annual CH4 emissions in China from 2019 to 2022 is 81 Tg, with the lowest emissions in 2021 (75 Tg) due to the impact of COVID-19. In 2021, the largest anthropogenic emissions in China are from agriculture, energy activities and livestock, accounting for 28% (20.8 Tg), 25% (18.9 Tg) and 19% (13.9 Tg) of total emissions, respectively, while wetlands, as the largest natural source, produce 14% (10.5 Tg) of CH4 emissions.

Clues of climate change on the Alashan Plateau since the last glacial period (40 ka) are important for revealing the mechanism of desertification of middle-latitude deserts in the Northern Hemisphere (NH). Studies are still rare for the understanding of the specific relationship of climate changes between the Alashan Plateau and the global. Based on a systematic and comparative analysis of the existing research in China and the international academic community, this paper reviews the environmental evolution history of the Alashan Plateau since the last glacial period from the records of paleo-environment and geomorphological characteristics in different deserts of the plateau (e.g., Badanjilin, Tenggeli, and Wulanbuhe). From about 40 ka to the end of the last glacial maximum, the climate on the plateau was wetter than it is today, and to the end of the Pleistocene, the climate was generally dry and the aeolian activities were enhanced. However, the climate was arid during the whole last glacial period in the Wulanbuhe Desert, evidently different from the overall pattern of the plateau. The Tenggeli Desert was characterized by an arid climate in the early Holocene. The most controversial events for the Alashan Plateau are the drought events in the middle Holocene in the Badanjilin Desert. The role and impact of the westerlies and the East Asian Summer Monsoon (EASM) systems on the climate change of the desert and even the whole plateau is a vexed question that brings different views in different periods. There is still a lack of definite evidence representing the events of global environmental change that occurred on the plateau during the discussed period. The distinctive morphology of dune mountains and the distribution of sand dunes are mutually indicative of the direction and energy of wind systems on the plateau. It is suggested that appropriate wind energy is the significant key to the desertification in these middle-latitude deserts on the plateau. From a global-scale review of climate change, the desertification of the modern-scale sandy desert landscapes on the Alashan Plateau is generally related to the global glacial period and the cold and dry climate during the past 40 ka.

The wind-induced responses of tall buildings are stochastic processes, and the peak factor is an important parameter to evaluate the extreme value of the wind-induced response in wind-resistant design. The existing research on the peak factor mainly focuses on the wind pressure on the building surface, but rarely concerns the wind-induced response peak factor of the structures. In view of this, the peak factor of the wind-induced response of super-high-rise buildings was studied in this paper. Firstly, a series of wind tunnel tests of the multi-degree-of-freedom aero-elastic models (MDOF) were carried out, wherein the along-wind and cross-wind responses were measured. Thereafter, the peak factor of wind-induced response was calculated using the peak factor method, classical extreme value theory, and the improved peak factor method. It was found that the peak factor calculated by the improved peak factor method is in good agreement with classical extreme value theory, which indicates that the improved peak factor method is applicable to calculate the peak factor of the wind-induced response of high-rise buildings. The results calculated using the improved peak factor method show that the peak factor of cross-wind response varies significantly with the wind speed, varying from about 2.5 to 5.5. The peak factor of cross-wind response first increases and then decreases with the increase in the wind speed, reaches the minimum near the critical wind speed of vortex-induced vibration (VIV), and increases again when the wind speed is larger than the VIV wind speed. Finally, an empirical formula for the cross-wind response peak factor was proposed as a function of the reduced wind speed, aspect ratio, and damping ratio of the structure.

This paper reports a comparative experiment using remote sensing underlying surface data (ESACCI) and Community Land Model underlying surface data (CLM_LS) to analyze the uncertainty of land surface types in land–atmosphere interaction. The results showed that the global distribution of ESACCI cropland is larger than that of CLM_LS, and there is a great degree of difference in some regions, which can reach more than 50% regionally. Furthermore, the changes of the underlying surface conditions can be transmitted to the model results through the data itself, resulting in the uncertainty of the surface energy balance, surface micro-meteorological elements, and surface water balance simulated by the model, which further affects the climate simulation effect.

The identification of parameters that can quantitatively describe the different characteristics of urban morphology is fundamental to studying urban ventilation and microclimate at the local level and developing parameterizations of the dynamic effect of an urban area in mesoscale models. This paper proposes a methodology to calculate four morphological parameters, namely mean height, aspect ratio, sky view factor, and plan area ratio, of five cities located in southern (Bari and Lecce), central (Naples and Rome), and northern (Milan) Italy. The calculation is performed using the Geographical Information System (GIS), starting from morphological and land use data collected and analyzed in shapefiles. The proposed methodology, which can be replicated in other cities, also presents in detail the procedure followed to properly build input data to calculate the sky view factor using the UMEP GIS tool. The results show a gradual increase in the plan area index, λp, and mean building height, H¯, moving from the south to the north of Italy. Maximum values of λp and H¯ are obtained in the regions of Milan, Rome, and Naples, where the highest spatially-averaged values are also found, i.e., λp = 0.22, H¯ = 10.9 m in Milan; λp = 0.19, H¯ = 12.7 m in Rome; λp = 0.20, H¯ = 12 m in Naples. Furthermore, for all the cities investigated, areas characterized by the Corine Land Cover class as “continuous urban fabric” are those with medium sky view factor SVF values (around 0.6–0.7) and λp values (around 0.3) typical of intermediate/compact cities. The methodology employed here for calculating morphological parameters using GIS proves to be replicable in different urban contexts. This opens to a better classification of cities in local climate zones (LCZ), as shown for the Lecce region, useful for urban heat island (UHI) studies and to the development of parameterizations of the urban effects in global and regional climate models.

The concentration of carbon dioxide and methane in the atmosphere has significantly increased over the last 60 years. One of the factors in the growth of methane and its homologue emissions is the intense thawing of gas hydrates, mainly from the Arctic shelf, which remains one of the less studied sources of atmospheric hydrocarbon emissions. Oxidation of methane and light-saturated hydrocarbons by ozone in the upper part of the atmosphere leads to the formation of CO2. The analysis of several datasets presented in this paper allows us to find the correlation between CH4 and CO2 concentrations in the atmosphere. This finding suggests that methane and its homologues released from gas hydrates mainly in the Arctic shelf zone become a significant source of carbon dioxide in the atmosphere. Because the amount of hydrocarbons located in gas hydrate deposits on the Arctic shelf is huge, further evolution of this process can become a serious challenge.

The paper deals with the measurement of individual size components of particulate matter focusing on typical particle size, mass, and number concentrations primarily in the city of Košice (Slovak republic) and the surrounding countryside. The deployment of the sensor IPS-7100 in the 2nd half of December 2022 allowed us to measure the particles smaller than the detection limit of other low-cost optical sensors—namely SPS30 and SEN54. The results show that although the mass concentration of ultrafine particles is negligible in comparison to fine and coarse particles, in terms of number concentration ultrafine particles make up the dominant component of particulate matter, which stands as a warning from the health point of view.

Energetic electron precipitation (EEP) is associated with solar activity and space weather and plays an important role in the Earth’s polar atmosphere. Energetic electrons from the radiation belt precipitate into the atmosphere during geomagnetic disturbances and cause additional ionization rates in the polar middle atmosphere. These induced atmospheric ionization rates lead to the formation of radicals in ion-molecular reactions at the heights of the mesosphere and upper stratosphere with the formation of reactive compounds of odd nitrogen NOy and odd hydrogen HOx groups. These compounds are involved in catalytic reactions that destroy the ozone. In this paper, we present the calculation of atmospheric ionization rates during geomagnetic disturbances using reconstructed spectra of electron precipitation from balloon observations; estimation of ozone destruction during precipitation events using one-dimensional photochemical radiation-convective models, taking into account both parameterization and ion chemistry; as well as provide an estimation of electron density during these periods.

Groundwater is a primary freshwater resource for human consumption and an essential source for industry and agriculture. Therefore, understanding its spatial and temporal trends and drivers is crucial for governments to take appropriate measures to manage water resources. This paper uses Gravity Recovery and Climate Experiment (GRACE) satellite data and the Global Land Data Assimilation System (GLDAS) to derive groundwater storage anomalies (GWSAs) and to analyze the spatial and temporal trends of GWSA in different regions of China (Xinjiang, Tibet, Inner Mongolia, North China Plain, South China, and Northeast China). It used groundwater-level observation data to verify the accuracy of GWSA estimates and analyzed the drivers of regional GWSA changes. The results showed that: (1) GWSA in South China increased at a rate of 4.79 mm/a from 2003 to 2016, and GWSA in other regions in China showed a decreasing trend. Among them, the decline rates of GWSA in Xinjiang, Tibet, Inner Mongolia, North China Plain, and Northeast China were −6.24 mm/a, −3.33 mm/a, −3.17 mm/a, −7.35 mm/a, and −0.75 mm/a, respectively. (2) The accuracy of the annual-scale GWSA estimates was improved after deducting gravity losses due to raw coal quality, and the correlation coefficient between GWSA and groundwater levels monitored by observation wells increased. (3) In Xinjiang, the annual water consumed by raw coal mining, industrial, and agricultural activities had a greater impact on GWSA than rainfall and temperature, so these human activities might be the main drivers of the continued GWSA decline in Xinjiang. Water consumption by raw coal mining and industry might be the main drivers of the continued decline in GWSA in Inner Mongolia and the North China Plain. The increase in groundwater storage in South China was mainly due to the recharge of rainfall.

This paper describes an ocean reanalysis system in the Indian and Pacific oceans (IPORA) and evaluates its quality in detail. The assimilation schemes based on ensemble optimal interpolation are employed in the hybrid coordinate ocean model to conduct a long-time reanalysis experiment during the period of 1993–2020. Different metrics including comparisons with satellite sea surface temperature, altimetry data, observed currents, as well as other reanalyses such as ECCO and SODA are used to validate the performance of IPORA. Compared with the control experiment without assimilation, IPORA greatly reduces the errors of temperature, salinity, sea level anomaly, and current fields, and improves the interannual variability. In contrast to ECCO and SODA products, IPORA captures the strong signals of SLA variability and reproduces the linear trend of SLA very well. Meanwhile, IPORA also shows a good consistence with observed currents, as indicated by an improved correlation and a reduced error.

In this paper, we propose a secure blockchain-aware framework for distributed data management and monitoring. Indeed, images-based data are captured through drones and transmitted to the fog nodes. The main objective here is to enable process and schedule, to investigate individual captured entity (records) and to analyze changes in the blockchain storage with a secure hash-encrypted (SH-256) consortium peer-to-peer (P2P) network. The proposed blockchain mechanism is also investigated for analyzing the fog-cloud-based stored information, which is referred to as smart contracts. These contracts are designed and deployed to automate the overall distributed monitoring system. They include the registration of UAVs (drones), the day-to-day dynamic captured drone-based images, and the update transactions in the immutable storage for future investigations. The simulation results show the merit of our framework. Indeed, through extensive experiments, the developed system provides good performances regarding monitoring and management tasks.

Dust emission from the Gobi desert is one of the major sources of global atmospheric aerosols. However, the main factors affecting dust emission from Gobi remain poorly understood. In this paper, field wind tunnel experiments were performed atop the Mogao Grottoes to determine the variation characteristics of the vertical dust flux (F) of particulate matter less than 10 μm (PM10) for Gobi surfaces with different dust content and wind speeds under external sand supply. The results demonstrate that F obeyed a power function with increasing friction velocity (U∗), and increased exponentially with the increasing surface dust content (C). The index of n-value in the formula F∝U∗n is taken in the range of 2.02–2.63 under the surface of 27.3–47.3% dust content (&lt, 100 µm), and the dust emission rate was significantly enhanced when the surface dust content exceeded approximately 37%. This study indicates that wind force is the primary dynamic condition affecting Gobi dust emission, and that surface dust content is a significant factor in determining the quantity of dust emission. Furthermore, the contribution of wind force to PM10 emission is greater than the surface dust content, and the higher the height, the greater the weight of friction velocity.

For many years, the Polish air quality modelling system was decentralized, which significantly hampered the appropriate development of methodologies, evaluations, and comparisons of modelling results. The major contributor to air pollution in Poland is the residential combustion sector. This paper demonstrates a novel methodology for residential emission estimation utilized for national air quality modelling and assessment. Our data were compared with EMEP and CAMS inventories, and despite some inequalities in country totals, spatial patterns were similar. We discuss the shortcomings of the presented method and draw conclusions for future improvements.

In recent decades, efforts to investigate atmospheric circulation patterns have predominantly relied on either semi-empirical datasets (i.e., reanalyses) or modeled output (i.e., global climate models, GCMs). While both approaches can provide important insights, there is a need for more empirical data to supplement these approaches. In this paper, we demonstrate how the application of relatively simple calculations to the basic measurements from a standard weather balloon radiosonde can provide a vertical profile of the horizontal atmospheric mass fluxes. These mass fluxes can be resolved into their meridional (north/south) and zonal (east/west) components. This provides a new useful empirical tool for analyzing atmospheric circulations. As a case study, we analyze the results for a selected five stations along a fairly constant meridian in the North Atlantic sector from 2015–2019. For each station, we find the atmospheric mass flux profiles from the lower troposphere to mid-stratosphere are surprisingly coherent, suggesting stronger interconnection between the troposphere and stratosphere than previously thought. Although our five stations span a region nominally covered by the classical polar, Ferrel and Hadley meridional circulation cells, the results are inconsistent with those expected for polar and Ferrel cells and only partially consistent with that of a Hadley cell. However, the region is marked by very strong prevailing westerly (west to east) mass fluxes for most of the atmosphere except for the equatorial surface easterlies (“trade winds”). We suggest that the extension of the techniques of this case study to other stations and time periods could improve our understanding of atmospheric circulation patterns and their time variations.

The particulate matter PM10 concentrations have been impacting hospital admissions due to respiratory diseases. The air pollution studies seek to understand how this pollutant affects the health system. Since prediction involves several variables, any disparity causes a disturbance in the overall system, increasing the difficulty of the models’ development. Due to the complex nonlinear behavior of the problem and their influencing factors, Artificial Neural Networks are attractive approaches for solving estimations problems. This paper explores two neural network architectures denoted unorganized machines: the echo state networks and the extreme learning machines. Beyond the standard forms, models variations are also proposed: the regularization parameter (RP) to increase the generalization capability, and the Volterra filter to explore nonlinear patterns of the hidden layers. To evaluate the proposed models’ performance for the hospital admissions estimation by respiratory diseases, three cities of São Paulo state, Brazil: Cubatão, Campinas and São Paulo, are investigated. Numerical results show the standard models’ superior performance for most scenarios. Nevertheless, considering divergent intensity in hospital admissions, the RP models present the best results in terms of data dispersion. Finally, an overall analysis highlights the models’ efficiency to assist the hospital admissions management during high air pollution episodes.

Precipitation is considered a crucial component in the hydrological cycle and changes in its spatial pattern directly influence the water resources. We compare different interpolation techniques in predicting the spatial distribution pattern of precipitation in Chongqing. Six interpolation methods, i.e., Inverse Distance Weighting (IDW), Radial Basis Function (RBF), Diffusion Interpolation with Barrier (DIB), Kernel Interpolation with Barrier (KIB), Ordinary Kriging (OK) and Empirical Bayesian Kriging (EBK), were applied to estimate different rainfall patterns. Annual mean, rainy season and dry-season precipitation was calculated from the daily precipitation time series of 34 meteorological stations with a time span of 1991 to 2019, based on Leave-One-Out Cross-Validation (LOOCV), Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Symmetric Mean Absolute Percentage Error (SMAPE) and Nash–Sutcliffe Efficiency coefficient (NSE) as validation indexes of the applied models for calculating the error degree and accuracy. Correlation test and Spearman coefficient was performed on the estimated and observed values. A method combining Entropy Weight and Technique for Order Preference by Similarity to Ideal Solution (Entropy-Weighted TOPSIS) was introduced to rank the performance of six interpolation methods. The results indicate that interpolation technique performs better in estimating during periods of low precipitation (i.e., dry season, relative to rainy season and mean annual). The performance priorities of the six methods under the combined multiple precipitation distribution patterns are KIB &gt, EBK &gt, OK &gt, RBF &gt, DIB &gt, IDW. Among them, KIB method has the highest accuracy which maps more accurate precipitation surfaces, with the disadvantage that estimation error is prone to outliers. EBK method is the second highest, and IDW method has the lowest accuracy with a high degree of error. This paper provides information for the application of interpolation methods in estimating rainfall spatial pattern and for water resource management of concerned regions.

The paper presents the results of a national radon survey conducted in the context of the Republic of Moldova. The study included about 2500 homes of different types, located in urban and rural localities in different areas of the country. The RADTRAK2 detectors kindly provided by the RADONOVA laboratory, Uppsala, Sweden, for the MOL9007 project funded by the International Atomic Energy Agency (IAEA) have been used. The exposure period was 90 days. The measured radon concentrations are the following: the average (±standard deviation) 252.8 ± 215.9 Bq/m3, the range (minimum–maximum) 10–1480 Bq/m3, the median 200 Bq/m3 and the geometric mean (± geometric standard deviation) 158.5 ± 3.0 Bq/m3. The results of the study allowed mapping indoor radon and identifying localities with increased radon concentrations. In order to reduce the high radon concentrations in places recording &gt, 300 Bq/m3, appropriate measures have been proposed according to the legislation in force. The cluster analysis showed positive links between the radon concentration in dwellings and the incidence of respiratory diseases, especially the bronchopulmonary cancer in the country.

Nowadays, simulation modeling is a relevant and practically significant means in the field for research of infrastructure object functioning. It forms the basis for studying the most important components of such objects represented by their digital twins. Applying meteorological data, in this context, becomes an important issue. In the paper, we propose a new microservice-based approach for organizing simulation modeling in heterogeneous distributed computing environments. Within the proposed approach, all operations related to data preparing, executing models, and analyzing the obtained results are implemented as microservices. The main advantages of the proposed approach are the parameter sweep computing within simulation modeling and possibility of integrating resources of public access supercomputer centers with cloud and fog platforms. Moreover, we provide automated microservice web forms using special model specifications. We develop and apply the service-oriented tools for studying environmentally friendly equipment of the objects at the Baikal natural territory. Among such objects are recreation tourist centers, children’s camps, museums, exhibition centers, etc. As a result, we have evaluated the costs for the possible use of heat pumps in different operational and meteorological conditions for the typical object. The provided comparative analysis has confirmed the aforementioned advantages of the proposed approach.

Nature-based solutions such as green infrastructure present an opportunity to reduce air pollutant concentrations and greenhouse gas emissions. This paper presents new findings from a controlled field study in Ontario, Canada, evaluating the impact of productive applications of green infrastructure on air pollution and carbon dioxide concentrations across different agricultural morphologies compared to other non-productive applications. This study demonstrates that productive green infrastructure applications are as beneficial as non-productive applications in reducing ozone, nitrogen dioxide, and carbon dioxide concentrations. Nature-based solutions present an opportunity to build climate resilience into agricultural systems through supply-side mitigation and adaptation. The implementation of productive green infrastructure could be a viable agricultural practice to address multiple climate change impacts.

The emission of SO2 from ships is an important source of atmospheric pollution. Therefore, the International Maritime Organization (IMO) has established strict requirements for the sulfur content of marine fuel oil. In this paper, a new optical noncontact detection technique for ship exhaust emissions analysis is studied. Firstly, the single-band simulation analysis model of the imaging detection technology for SO2 concentration in ship exhaust gas and the deep neural network model for the prediction of sulfur content were established. A bench test was designed to monitor the tail gas concentration simultaneously using online and imaging detection methods, so as to obtain the concentration data in the flue and the ultraviolet image data. The results showed that 300 nm had a higher inversion accuracy than the other two bands. Finally, a deep neural network model was trained with the SO2 concentration data from the inversion and the engine power, and the predictive model of sulfur content in marine fuel oil was thereby obtained. When the deep learning model was used to predict sulfur content, the prediction accuracy at 300, 310, and 330 nm was 73%, 94%, and 71%, respectively.