Search

The present work shows the experimental evidence carried out on a pilot scale and demonstrating the potential of Cannabis sativa L. by-products for biogas production through anaerobic digestion. While the current state-of-the-art tests on anaerobic digestion feasibility are carried out at the laboratory scale, the here described tests were carried out at a pilot-to-large scale. An experimental campaign was carried out on hemp straw residues to assess the effective performance of this feedstock in biogas production by reproducing the real operating conditions of an industrial plant. An organic loading rate was applied according to two different amounts of hemp straw residues (3% wt/wt and 5% wt/wt). Also, specific bioenhancers were used to maximize biogas production. When an enzymatic treatment was not applied, a higher amount of hemp straw residues determined an increase of the median values of the gas production rate of biogas of 92.1%. This reached 116.6% when bioenhancers were applied. The increase of the specific gas production of biogas due to an increment of the organic loading rate (5%wt/wt) was +77.9% without enzymatic treatment and it was +129.8% when enzymes were used. The best management of the biodigester was found in the combination of higher values of hemp straw residues coupled with the enzymatic treatment, reaching 0.248 Nm3&middot, kgvolatile solids-1 of specific biogas production. Comparisons were made between the biogas performance obtained within the present study and those found in the literature review coming from studies on a laboratory scale, as well as those related to the most common energy crops. The hemp straw performance was similar to those provided by previous studies on a laboratory scale. Values reported in the literature for other lignocellulosic crops are close to those of this work. Based on the findings, biogas production can be improved by using bioenhancers. Results suggest an integration of industrial hemp straw residues as complementary biomass for cleaner production and to contribute to the fight against climate change.

The requirement to monitor and control industrial processes has increased over recent years, therefore innovative techniques are required to meet the demand for alternative methods of particulate measurement. Resonant mass sensors are now strong candidates for accurate mass measurement and are frequently used in many diverse fields of science and engineering. This paper presents the design, modelling, and optimal geometry selection for sensitivity improvement of a U-shaped glass tube as a resonant mass sensing cantilever with a view to becoming a component of particulate measurement equipment. Finite Element Analysis (FEA) was used to develop the system which was validated experimentally using a physical model. This paper focuses on both the proof of concept and the geometry selection of the sensor using analysis of the system sensitivity for best selection. Modal and harmonic analysis were undertaken across a range of commercially available glass tube sizes from 6 mm to 10 mm diameter, to determine the optimal geometry selection, validated with practical experimental data. Results show a consistent difference of 3–5% between the simulation and experimental results, showing strong correlation. This research provides a methodology on the development of using a U-shaped glass tube for accurate mass measurement with a view to exploring the design as a component of particulate emissions equipment. The experimental and simulation results confirm that the highest sensitivity is achieved when the geometry dimensions, and therefore the vacant mass of the tube, is reduced. The 6 mm diameter tube with the smallest bend radius was the most suitable design to meet the design criteria. The calibration curve was plotted to allow an unknown mass to be calculated, which gave an R2 value of 0.9984. All experimental work was repeated three times with results giving an average of 0.44% between the minimum and maximum showing strong linearity and suggesting the potential for implementation of the methodology in its intended application. The design provides possible solutions to some of the issues currently seen with particulate measurement from stationary sources.

With the rapid development of urbanization, ambient air pollution is becoming increasingly serious. Out of many pollutants, fine particulate matter (PM2.5) is the pollutant that affects the urban atmospheric environment to the greatest extent. Fine particulate matter (PM2.5) concentration prediction is of great significance to human health and environmental protection. This paper proposes a CNN-SSA-DBiLSTM-attention deep learning fusion model. This paper took the meteorological observation data and pollutant data from eight stations in Bijie from 1 January 2015 to 31 December 2022 as the sample data for training and testing. For the obtained data, the missing values and the data obtained from the correlation analysis performed were first processed. Secondly, a convolutional neural network (CNN) was used for the feature selection. DBILSTM was then used to establish a network model for the relationship between the input and actual output sequences, and an attention mechanism was added to enhance the impact of the relevant information. The number of units in the DBILSTM and the epoch of the whole network were optimized using the sparrow search algorithm (SSA), and the predicted value was the output after optimization. This paper predicts the concentration of PM2.5 in different time spans and seasons, and makes a comparison with the CNN-DBILSTM, BILSTM, and LSTM models. The results show that the CNN-SSA-DBiLSTM-attention model had the best prediction effect, and its accuracy improved with the increasing prediction time span. The coefficient of determination (R2) is stable at about 0.95. The results revealed that the proposed CNN-SSA-DBiLSTM-attention ensemble framework is a reliable and accurate method, and verifies the research results of this paper in regard to the prediction of PM2.5 concentration. This research has important implications for human health and environmental protection. The proposed method could inspire researchers to develop even more effective methods for atmospheric environment pollution modeling.

General circulation models (GCMs) run at regional resolution or at a continental scale. Therefore, these results cannot be used directly for local temperatures and precipitation prediction. Downscaling techniques are required to calibrate GCMs. Statistical downscaling models (SDSM) are the most widely used for bias correction of GCMs. However, few studies have compared SDSM with multi-layer perceptron artificial neural networks and in most of these studies, results indicate that SDSM outperform other approaches. This paper investigates an alternative architecture of neural networks, namely the long-short-term memory (LSTM), to forecast two critical climate variables, namely temperature and precipitation, with an application to five climate gauging stations in the Lake Chad Basin. Lake Chad is a data scarce area which has been impacted by severe drought, where water resources have been influenced by climate change and recent agricultural expansion. SDSM was used as the benchmark in this paper for temperature and precipitation downscaling for monthly time–scales weather prediction, using grid resolution GCM output at a 5 degrees latitude × 5 degrees longitude global grid. Three performance indicators were used in this study, namely the root mean square error (RMSE), to measure the sensitivity of the model to outliers, the mean absolute percentage error (MAPE), to estimate the overall performance of the predictions, as well as the Nash Sutcliffe Efficiency (NSE), which is a standard measure used in the field of climate forecasting. Results on the validation set for SDSM and test set for LSTM indicated that LSTM produced better accuracy on average compared to SDSM. For precipitation forecasting, the average RMSE and MAPE for LSTM were 33.21 mm and 24.82% respectively, while the average RMSE and MAPE for SDSM were 53.32 mm and 34.62% respectively. In terms of three year ahead minimum temperature forecasts, LSTM presents an average RMSE of 4.96 degree celsius and an average MAPE of 27.16%, while SDSM presents an average RMSE of 8.58 degree celsius and an average MAPE of 12.83%. For maximum temperatures forecast, LSTM presents an average RMSE of 4.27 degree celsius and an average MAPE of 11.09 percent, while SDSM presents an average RMSE of 9.93 degree celsius and an average RMSE of 12.07%. Given the results, LSTM may be a suitable alternative approach to downscale global climate simulation models’ output, to improve water management and long-term temperature and precipitations forecasting at local level.

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.

Public participation is playing an increasingly important role in environmental governance. This paper uses panel data from Chinese cities to evaluate the causal relationship between public participation, regional carbon emissions, and regional carbon intensity. We obtain the following conclusions: (1) Public participation significantly reduces regional carbon emissions and regional carbon intensity, which remains robust after a series of robustness and endogeneity discussions. (2) The carbon reduction effect of public participation performs better in eastern regions, regions with higher per capita income, and regions with a concentration of tertiary industries and talents. (3) We divided the public participation into resident participation and environment non-governmental organizations (ENGOs) participation. We found an excellent interactive emission reduction effect with resident participation and a good interaction between resident participation and government and environmental organizations. (4) This paper finds that promoting regional green technology innovation is a significant mechanism for public participation in achieving carbon emission reduction. (5) Finally, this paper found an “inverted U-shaped” non-linear relationship between public participation and regional carbon emissions. The results reveal the importance of public participation in regional carbon emissions and provide an empirical basis for promoting informal environmental regulation.

The Tarim Basin is a large inland arid basin in the arid region of northwest China and has been experiencing significant “warming and wetting” since 1987. As a result, the purpose of this paper is to determine whether the climate transition phenomenon occurred in the Tarim Basin as well as the role of atmospheric circulation in this process. We use meteorological data and atmospheric circulation indexes to study the seasonal trends of climate change in this region from 1987 to 2020 to understand how they are affected by atmospheric circulation. The findings show that, from 1987 to 2020, the Tarim Basin experienced significant warming and wetting; with the exception of the winter scale, all other seasonal scales exhibited a clear warming and wetting trend. From the perspective of spatial distribution, most of the areas showed a significant warming trend, and the warming amplitude around the basin is greater than that in the central area of the basin. However, there are significant regional differences in precipitation change rates. Meanwhile, wavelet analysis shows that there is a significant oscillation period of 17–20 years between climate change and the atmospheric circulation index during 1987–2020. The correlation analysis shows that the Pacific decadal oscillation (PDO) and El Niño-Southern Oscillation (ENSO) are the main influencing factors of climate change in the Tarim Basin at different seasonal scales, while the teleconnection of the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) is low and the PDO dominates the summer and autumn temperature changes in the Tarim Basin. The research results of this paper show that, despite the warming and wetting trends since 1987 in the Tarim Basin, the climate type did not change. From 1987 to 2020, the main teleconnection factors of climate change in the Tarim Basin were PDO and ENSO.

Accurate prediction of air temperature is of great significance to outdoor activities and daily life. However, it is important and more challenging to predict air temperature in complex terrain areas because of prevailing mountain and valley winds and variable wind directions. The main innovation of this paper is to propose a regional temperature prediction method based on deep spatiotemporal networks, designing a spatiotemporal information processing module to align temperature data with regional grid points and further transforming temperature time series data into image sequences. Long Short-Term Memory network is constructed on the images to extract the depth features of the data to train the model. The experiments demonstrate that the deep learning prediction model containing the spatiotemporal information processing module and the deep learning prediction module is fully feasible in short-term regional temperature prediction. The comparison experiments show that the model proposed in this paper has better prediction results for classical models, such as convolutional neural networks and LSTM networks. The experimental conclusion shows that the method proposed in this paper can predict the distribution and change trend of temperature in the next 3 h and the next 6 h on a regional scale. The experimental result RMSE reached 0.63, showing high stability and accuracy. The model provides a new method for local regional temperature prediction, which can support the planning of production and life in advance and tend to save energy and reduce consumption.

The paper took Guang’an City in Sichuan Province of China as a research region, and it built an evaluation index system based on 14 evaluation indices according to four criteria, namely, ecological background, ecological structure, ecological benefits, and ecological stress. The Delphi algorithm and entropy weight (a combination of subjective and objective methods) were used to calculate the weight value of each evaluation index, and an ideal point model was used to calculate the ideal point value. The ideal point ecological grade was classified, and the principal component analysis method was used to obtain the main control factors of each year, which revealed the relationship between the ecological distribution of ideal points and the environmental impact factor, with the changing characteristics of the ecology in the research region according to the hotspot model of the spatial differentiation of land ecological quality as the main control factor. Lastly, the paper also analyzed the land ecological quality of Guangan City in 2000, 2005, 2010, and 2015. The study results show that the overall land ecological quality in Guang’an City generally increased from 2005 to 2015. The proportion of forest land area and the temperature were the most important main control factors. The air temperature and the land ecological quality were positively correlated.

The prediction of the total electron content (TEC) in the ionosphere is of great significance for satellite communication, navigation and positioning. This paper presents a multiple-attention mechanism-based LSTM (multiple-attention Long Short-Term Memory, MA-LSTM) TEC prediction model. The main achievements of this paper are as follows: (1) adding an L1 constraint to the LSTM-based TEC prediction model—an L1 constraint prevents excessive attention to the input sequence during modelling and prevents overfitting; (2) adding multiple-attention mechanism modules to the TEC prediction model. By adding three parallel attention modules, respectively, we calculated the attention value of the output vector from the LSTM layer, and calculated its attention distribution through the softmax function. Then, the vector output by each LSTM layer was weighted and summed with the corresponding attention distribution so as to highlight and focus on important features. To verify our model’s performance, eight regions located in China were selected in the European Orbit Determination Center (CODE) TEC grid dataset. In these selected areas, comparative experiments were carried out with LSTM, GRU and Att-BiGRU. The results show that our proposed MA-LSTM model is obviously superior to the comparison models. This paper also discusses the prediction effect of the model in different months. The results show that the prediction effect of the model is best in July, August and September, with the R-square reaching above 0.99. In March, April and May, the R-square is slightly low, but even at the worst time, the fitting degree between the predicted value and the real value still reaches 0.965. We also discussed the influence of a magnetic quiet period and a magnetic storm period on the prediction performance. The results show that in the magnetic quiet period, our model fit very well. In the magnetic storm period, the R-square is lower than that of the magnetic quiet period, but it can also reach 0.989. The research in this paper provides a reliable method for the short-term prediction of ionospheric TEC.

Caribbean Small Island Developing States (SIDS) is one of the most vulnerable regions in the world to the impacts of climate change. The region has prioritized adaptation to climate change and has implemented many adaptation actions over the past 20 years. However, the region is becoming increasingly vulnerable to the impacts of tropical cyclones (TC). This paper analyses the impacts of TC on the region between 1980 to 2019. It aims to examine the economic loss and damage sustained by the region, identify the sectors most impacted, and ascertain the perspectives of key stakeholders on the factors that hinder building resilience. Statistical analysis techniques and semi-structured interviews were to unpack and understand the dataset. The paper finds that economic loss and damage has gradually increasing between 1980 to 2009 with a drastic increase between 2010 to 2019. The paper highlights the agriculture, housing, transport, and utility sectors as the most impacted. The findings also call to attention the need for increased access to adaptation financing for SIDS, the disadvantages of the income status that hinders building resilience, and the need for increased Early Warning Systems. The paper recommends revising the per capita national income as an eligibility criterion for accessing concessional development finance assistance, a comprehensive EWS for the countries in the region, and consideration of debt relief for countries affected by TC.

With the continuous growth of international maritime trade, black carbon (BC) emissions from ships have caused great harm to the natural environment and human health. Controlling the BC emissions from ships is of positive significance for Earth’s environmental governance. In order to accelerate the development process of ship BC emission control technologies, this paper proposes a BC emission prediction model based on stacked generalization (SG). The meta learner of the prediction model is Ridge Regression (RR), and the base learner combines four models: Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGB), Random Forest (RF), and Support Vector Regression (SVR). We used mutual information (MI) to measure the correlation between combustion characteristic parameters (CCPs) and BC emission concentration, and selected them as the features of the prediction model. The results show that the CCPs have a strong correlation with the BC emission concentration of the diesel engine under different working conditions, which can be used to describe the influence of the changes to the combustion process in the cylinder on the BC generation. The introduction of the stacked generalization method reconciles the inherent bias of various models. Compared with traditional models, the fusion model has achieved higher prediction accuracy on the same datasets. The research results of this paper can provide a reference for the research and development of ship black carbon emission control technologies and the formulation of relevant regulations.

To reduce greenhouse gas emissions, nations have introduced energy efficiency regulations for new and existing buildings. This has been considered advantageous as more efficient building envelopes would reduce energy consumed to heat and cool home interiors to within accepted thermal comfort bandwidths. However, as these methods have been adopted, many nations have identified an unintended visible presence of surface and interstitial condensation and mould in new code-compliant buildings. In Australia, it has been estimated that up to 50% of Australian houses constructed in the last decade (2006–2016) have a presence of condensation and mould. Australia introduced its first condensation and mould-related building regulations for new homes in 2019. This paper reports on the hygrothermal and mould growth analysis of the most common low-rise residential external wall system, a timber-framed clay masonry veneer wall. A key component of this paper discusses the application of innovative methods in the Australian context. The external wall’s moisture accumulation and mould growth were simulated for a period of ten years using the transient hygrothermal simulation tool, WUFI® Pro, and the mould growth model, WUFI® VTT. This study identified significant risks for this typical external wall system when constructed in a temperate climate.

The main source of production in the middle and late stages of shale gas extraction is the adsorbed gas in shale, and the adsorbed gas in shale mainly comes from organic matter casein and clay minerals in shale; therefore, this paper uses sodium-based montmorillonite to characterize the clay minerals in shale and study the CH4 adsorption law in clay minerals, and this study has certain guiding significance for shale gas extraction. In addition, this paper also conducts a study on the competitive adsorption law of CH4 and CO2, and at the same time, predicts the theoretical sequestration of CO2 in shale clay minerals, which is a reference value for the study of CO2 burial in shale and is beneficial to the early realization of carbon neutral. In this paper, the slit model of sodium-based montmorillonite and the fluid model of CH4 and CO2 were constructed using Materials Studio software, and the following two aspects were studied based on the Monte Carlo method: Firstly, the microscopic adsorption behavior of CH4 in sodium-based montmorillonite was studied, and the simulations showed that the adsorption capacity of montmorillonite decreases with increasing temperature, increases and then decreases with increasing pressure, and decreases with increasing pore size. CH4 has two states of adsorption and free state in the slit. The adsorption type of CH4 in montmorillonite is physical adsorption. Secondly, the competitive adsorption of CH4 and CO2 in sodium-based montmorillonite was studied, and the simulations showed that the CO2 repulsion efficiency increased with increasing CO2 injection pressure, and the CO2/CH4 competitive adsorption ratio decreased with increasing pressure. The amount of CO2 storage decreased with increasing temperature and increased with increasing CO2 injection pressure.

The European Standard EN 15757: 2010 ‘Conservation of Cultural Property—Specifications for temperature and relative humidity to limit climate-induced mechanical damage in organic hygroscopic materials’ is a guide specifying the allowed limits of variability of the indoor climate, in particular relative humidity (RH) to preserve cultural heritage objects and collections composed of climate-vulnerable materials. This paper is finalized to provide useful elements to improve the Standard at its next revision, based on focused research. The methodologies and the mathematical tools used are performed on 18 case studies representing different buildings, climates, and use, including heated and unheated buildings, museums, churches, concert halls, archives, and storage rooms. The first aim is to compare the method based on the centred moving average suggested by Annex A of EN15757 with an alternative method based on percentile interpolation to calculate the reference RH values, and in particular the safe band of RH variability, as well as the upper and lower risky bands. It has been found that the two methods provided the same results, but the latter is easier to manage. The second aim is to verify if the duration of the record necessary for the determination of the safe band is really 13 months of measurements as required by the Standard to account for the specific request of the centred moving average with a 30-day time window. This paper demonstrates that the same goal may be reached with a 12-month record, but extracting from the record itself the two periods required by the time window, i.e., the last 15 days of the year will be copied before the start of the record, and the same with the first 15 days after the end. The third aim is to test if the particular choice of the width of the time window is influential on the width of the safe band, and to assess the relationship between the width of the safe band and the width of the time window. The results show that the safe band logarithmically depends on the length of the time window, so it is crucial to respect the 30-day window established by the Standard.

This paper investigates 7 MHz ionospheric radio wave propagation between pairs of distant countries that simultaneously lie on the terminator. This is known as greyline propagation. Observations of amateur radio beacon transmitters recorded in the Weak Signal Propagation Reporter (WSPR) database are used to investigate the times of day that beacon signals were observed during the year 2017. The WSPR beacon network consists of thousands of automated beacon transmitters and observers distributed over the globe. The WSPR database is a very useful resource for radio science as it offers the date and time at which a propagation path was available between two radio stations, as well as their precise locations. This paper provides the first systematic study of grey-line propagation between New Zealand/Eastern Australia and UK/Europe. The study shows that communications were predominantly made from the United Kingdom (UK) to New Zealand at around both sunset and sunrise times, whereas from New Zealand to the UK, communication links occurred mainly during UK sunrise hours. The lack of observations at the UK sunset time was particularly evident during the UK summer. The same pattern was found in the observations of propagation from Eastern Australia to UK, and from New Zealand and Eastern Australia to Italy and the surrounding regions in Europe. The observed asymmetry in reception pattern could possibly be due to the increase in electromagnetic noise across Europe in the summer afternoon/evening from thunderstorms.

This paper aims to study the path oscillations of single, spherical water droplets levitated in a wind tunnel to better comprehend the mechanical motion of small raindrops. The observations were carried out in the Mainz vertical wind tunnel. The discrete, fast Fourier transform was used to determine the oscillatory frequencies of the droplet paths, and the Hilbert transform was applied to analyze their instantaneous frequency stability. Both transversal and streamwise components of the path oscillations are described with typical frequencies of several tens of Hertz. The studied oscillations elongate the paths, reduce the terminal velocity of the smallest droplets, and make the rain droplet fall in a non-uniform motion even after reaching terminal velocity. The terminal velocity can be considered as physically having been reached if our proposed practical criterion is satisfied. From a fluid mechanics perspective, the paper fills an experimental gap in the studies of the paths oscillations of single, liquid spheres of microscopic sizes at low Bond numbers

With the exponential growth in the amount of available data, traditional meteorological data processing algorithms have become overwhelmed. The application of artificial intelligence in simultaneous prediction of multi-parameter meteorological data has attracted much attention. However, existing single-task network models are generally limited by the data correlation dependence problem. In this paper, we use a priori knowledge for network design and propose a multitask model based on an asymmetric sharing mechanism, which effectively solves the correlation dependence problem in multi-parameter meteorological data prediction and achieves simultaneous prediction of multiple meteorological parameters with complex correlations for the first time. The performance of the multitask model depends largely on the relative weights among the task losses, and manually adjusting these weights is a difficult and expensive process, which makes it difficult for multitask learning to achieve the expected results in practice. In this paper, we propose an improved multitask loss processing method based on the assumptions of homoscedasticity uncertainty and the Laplace loss distribution and validate it using the German Jena dataset. The results show that the method can automatically balance the losses of each subtask and has better performance and robustness.

High concentration levels of air pollutants may cause damage to plants, animals, and the health of some groups of human beings. Therefore, it is important to investigate different topics related to the high air pollution levels and to find reliable answers to the questions about the possible damages, which might take place when these levels exceed some limits. A few of the numerous questions, the answers of which are highly desirable, are listed below: (a) When are the air pollution levels dangerous? (b) What is the reason for the increased air pollution levels? (c) How can the air pollution levels be decreased? (d) Will the future climate changes result in higher and more dangerous air pollution levels? It is necessary to study carefully many issues connected with the distribution of air pollutants in a given region and with the reasons for the increases of the concentrations to high levels, which might be damaging. In order to do this, it is necessary to develop a Digital Twin of all relevant physical processes in the atmosphere and to use after that this tool in different applications. Such a tool, its name is DIGITAL AIR, has been created. Digital Twins are becoming more and more popular). Many complex problems, arising taking place in very complicated surroundings, can be handled and resolved successfully by applying Digital Twins. The preparation of such a digital tool as well as its practical implementation in the treatment of a special problem, the increase of some potentially dangerous ozone levels, will be discussed and tested in this paper. The Unified Danish Eulerian Model (UNI-DEM) is a very important part of DIGITAL AIR. This mathematical model, UNI-DEM, can be applied in many different studies related to damaging effects caused by high air pollution levels. We shall use it in this paper to get a reliable answer to a very special but extremely important question: will the future climatic changes lead to an increase in the ozone pollution levels in Bulgaria and Europe, which can potentially become dangerous for human health?

Due to the particularity of the site selection of hydropower stations, the canyon wind with large fluctuations often occurs during the construction of the hydropower station, which will seriously affect the safety of construction personnel. Especially in the early stage of the construction of the hydropower station, the historical data and information on the canyon wind are scarce. Short-term forecasting of canyon wind speed has become extremely important. The main innovation of this paper is to propose a time series prediction method based on transfer learning. This method can achieve short-term prediction when there are few wind speed sample data, and the model is relatively simple while ensuring the accuracy of prediction. Considering the temporal and nonlinear characteristics of canyon wind speed data, a hybrid transfer learning model based on a convolutional neural network (CNN) and gated recurrent neural network (GRU) is proposed to predict short-term canyon wind speed with fewer observation data. In this method, the time sliding window is used to extract time series from historical wind speed data and temperature data of adjacent cities as the input of the neural network. Next, CNN is used to extract the feature vector from the input, and the feature vector can form time series. Then, the GRU network is used for short-term wind speed prediction by the time series. Experimental results show that the proposed method improves MAE and RMSE by nearly 20%, which will provide new ideas for the application of wind speed forecasting in canyons under complex terrain. The research contents of this paper contribute to the actual construction of hydropower stations.

As the main energy source for thermal power generation, coal generates a large amount of NOx during its incineration in boilers, and excessive NOx emissions can cause serious pollution to the air environment. Selective catalytic reduction denitrification (SCR) selects the optimal amount of ammonia to be injected for denitrification based on the measurement of NOx concentration by the automatic flue gas monitoring system. Since the automatic flue gas monitoring system has a large delay in measurement, it cannot accurately reflect the real-time changes of NOx concentration at the SCR inlet when the unit load fluctuates, leading to problems such as ammonia escape and NOx emission exceeding the standard. In response to these problems, this paper proposes an SCR inlet NOx concentration prediction algorithm based on BMIFS-LSTM. An improved mutual information feature selection algorithm (BMIFS) is used to filter out the auxiliary variables with maximum correlation and minimum redundancy with NOx concentration, and reduce the coupling and dimensionality among the variables in the data set. The dominant and auxiliary variables are then fed together into a long short-term memory neural network (LSTM) to build a prognostic model. Simulation experiments are conducted using historical operation data of a 300 MW thermal power unit. The experimental results show that the algorithm in this paper reduces the average relative error by 3.45% and the root mean square error by 1.50 compared with the algorithm without auxiliary variable extraction, which can accurately reflect the real-time changes of NOx concentration at the SCR inlet, solve the problem of delay in NOx concentration measurement, and reduce the occurrence of atmospheric pollution caused by excessive NOx emissions.

The paper compares the emissions from vehicles including ICEVs (internal combustion engine vehicles) with equivalent emissions from BEVs (battery electric vehicles). Additionally, it analyzes the available source research and the specific energy mix for Poland based on carbon. Mathematical calculations estimate air pollutant emissions. To carry out the analysis and calculations, data were provided by the manufacturers of electricity consumption in the case of vehicles equipped with electric motors and the COPERT model for internal combustion engines. Air pollutants are considered: CO2, NOx, SOx, CO, and Total Suspended Particles (TSP). In addition to exhaust emissions, all solids emissions from road abrasion and tire and brake wear are also considered. The emission of pollutants is estimated based on the emission factors using the average mileage in Polish conditions. The paper compares emissions for three scenarios considering electric vehicles, combustion engine cars, and hybrid cars. Analyses show that introducing cars with electric engines into traffic at the expense of withdrawing vehicles with internal combustion engines is not favorable in Polish conditions. The analysis indicates that CO, CO2, and TSP emissions have decreased, while NOx and SOx emissions have increased.

Understanding the characteristics of storm surges is especially important in the context of ongoing climate changes, which often lead to catastrophic events in the coastal zones of seas and oceans. For this reason, this paper presents the characteristics of the Baltic Sea storm surges and trends in their occurrences through the past 60 years. The study material was based on hourly sea level readings, spanning the years 1961–2020, retrieved from 45 Baltic Sea tide gauges, as well as air pressure and wind field data. Owing to the analysis and visualization of storm situations, two main types of storm surges were identified and characterized: a surge driven by wind and a surge driven by subpressure associated with an active low pressure area. This paper also discusses a third, mixed type of storm surge. Further analyses have indicated that through the past 60 years in the Baltic Sea, the duration of high sea level has increased by 1/3, the average number of storm surges has increased from 3.1 to 5.5 per year, and the maximum annual sea levels have increased—with a trend value of 0.28 cm/year. These processes, also observed in other marine basins, provide strong evidence for contemporary climate change.

The impact of air pollution on human health is becoming increasingly severe, and economic losses are a significant impediment to economic and social development. This paper investigates the impact of air pollutants on the respiratory system and its action mechanism by using information on inpatients with respiratory diseases from two IIIA (highest) hospitals in Wuhan from 2015 to 2019, information on air pollutants, and meteorological data, as well as relevant demographic and economic data in China. This paper describes the specific conditions of air pollutant concentrations and respiratory diseases, quantifies the degree of correlation between the two, and then provides a more comprehensive assessment of the economic losses using descriptive statistical methods, the generalized additive model (GAM), cost of illness approach (COI), and scenario analysis. According to the findings, the economic losses caused by PM2.5, PM10, SO2, NO2, and CO exposure are USD 103.17 million, USD 70.54 million, USD 98.02 million, USD 40.35 million, and USD 142.38 million, for a total of USD 454.46 billion, or approximately 0.20% of Wuhan’s GDP in 2019. If the government tightens control of major air pollutants and meets the WHO-recommended criterion values, the annual evitable economic losses would be approximately USD 69.4 million or approximately 0.03% of Wuhan’s GDP in 2019. As a result, the relevant government departments must strengthen air pollution control to mitigate the impact of air pollution on population health and the associated economic losses.

This paper mainly focuses on the relationship between the subjective evaluation of air quality and the quality of life (QOL) of middle-aged and elderly residents in China. The 2018 China Health and Retirement Longitudinal Study (CHARLS) project database is the key sources of data, from which 16,736 valid samples were used in our research. Multivariate linear regression analysis and binomial logistic regression model were applied to detect the impact of the subjective evaluation of air quality on QOL, which was evaluated in two dimensions, which are health utility and experienced utility, using the health utility EQ-5D score and the experienced utility of life satisfaction score. Our results show that there is a significant positive correlation between the subjective evaluation of air quality and the two dimensions of QOL. Age, education, marital status and sleep status also have a relatively great impact on the QOL of residents. This worked studied the overall QOL of middle-aged and elderly residents in China, while policy suggestions regarding high-quality air public goods are also given in the paper.

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.

The aim of this paper was to analyze the impact of air pollution and meteorological conditions on the effectiveness of recreation in selected health resorts in Poland in 2015–2019. Four municipalities with the status of health resorts were compared in terms of exposure to harmful air pollutants such as PM10, NO2, SO2, and B(a)P in PM10. In this paper, a comprehensive statistical analysis was performed by determining the basic statistics of the measurement series. In addition, analyses of the occurrence of episodes of elevated PM10 concentrations in health resorts in Poland, as well as correlation and regression analyses, were performed. Statistical analysis showed no annual mean exceedances for the air pollutants analyzed. Average annual concentrations of harmful pollutants decreased year by year in Rabka Zdrój and also in Ciechocinek. The situation was different in Sopot and Ustroń, where the average annual pollution remained at a similar level and there was no downward trend. Studies have shown that travel to spa communities for health purposes can be problematic because air quality, while not exceeding average annual standards, is not satisfactory. To effectively address public health concerns, it is also necessary to consider meteorological conditions when analyzing air quality. A detailed analysis of the impact of meteorological conditions (average air temperature, wind speed, relative humidity, and visibility) on air quality, based on forecasts, will also help in the implementation of air protection plans and strengthen the control of harmful pollutant levels. Measures to reduce the levels of harmful pollutants will affect the effectiveness of patient treatment in spas. The article presents the correct way to conduct reliable monitoring of air quality and meteorological conditions, where it is particularly important.

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.

This bibliometric analysis deals with research on the decline and fall of the Western Roman Empire in connection with climate change. Based on the Web of Science (WoS) database, we applied a combination of three different search queries for retrieving the relevant literature: (1) on the decline and fall of the Roman Empire in general, (2) more specifically on the downfall in connection with a changing climate, and (3) on paleoclimatic research in combination with the time period of the Roman Empire and Late Antiquity. Additionally, we considered all references cited by an ensemble of selected key papers and all citing papers of these key papers, whereby we retrieved additional publications (in particular, books and book chapters). We merged the literature retrieved, receiving a final publication set of 85 publications. We analyzed this publication set by applying a toolset of bibliometric methods and visualization programs. A co-authorship map of all authors, a keyword map for a rough content analysis, and a citation network based on the publication set of 85 papers are presented. We also considered news mentions in this study to identify papers with impacts beyond science. According to the literature retrieved, a multitude of paleoclimatic data from various geographical sites for the time of late antiquity indicate a climatic shift away from the stability of previous centuries. Recently, some scholars have argued that drought in Central Asia and the onset of a cooler climate in North-West Eurasia may have put Germanic tribes, Goths, and Huns on the move into the Roman Empire, provoking the Migration Period and eventually leading to the downfall of the Western Roman Empire. However, climate is only one variable at play, a combination of many factors interacting with each other is a possible explanation for the pattern of long-lasting decline and final collapse. Currently, the number of records from different locations, the toolbox of suitable analytic methods, and the precision of dating are evolving rapidly, contributing to an answer for one of the most classic of all historical questions. However, these studies still lack the inevitable collaboration of the major disciplines involved: archeology, history, and climatology. The articles of the publication set analyzed mainly result from research in the geosciences.

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.

This bibliometric analysis deals with research on the collapse of the Maya civilization—a research topic with a long-lasting history, which has been boosted significantly by recent paleoclimatic research. The study is based on a publication set of 433 papers published between 1923 and 2016. The publications covered by the Web of Science (WoS) show a significant increase since 1990, reaching about 30 papers per year at present. The results show that the current discourse on the collapse of the Maya civilization is focused on the role of climate as a major factor for the demise of this ancient civilization. The bibliometric analyses also reveal that (1) paleoclimatic records become numerous and are increasingly better dated; (2) the explanatory power of the records has been significantly increased by analyzing samples from regions closer to the relevant Maya sites; and (3) interdisciplinary cooperation of the humanities (archeology, anthropology, history) with natural sciences disciplines (geoscience, ecology, paleoclimatology, meteorology) seems to be highly promising. The collapse of the Maya civilization is a good example of how natural sciences entered research in the humanities and social sciences (anthropology, archeology, history) and boosted research (and solutions) around long-discussed, but unsolved questions.

The Global Climate Model (GCM) run at a coarse spatial resolution cannot be directly used for climate impact studies. Downscaling is required to extract the sub-grid and local scale information. This paper investigates if the artificial neural network (ANN) is better than the widely-used regression-based statistical downscaling model (SDSM) for downscaling climate for a site in Colombo, Sri Lanka. Based on seasonal and annual model biases and the root mean squared error (RMSE), the ANN performed better than the SDSM for precipitation. This paper proposes a novel methodology for improving climate predictions by combining SDSM with neural networks. This method will allow a user to apply SDSM with a neural network model for higher skills in downscaling. The study uses the Canadian Earth System Model (CanESM2) of the IPCC Fifth Assessment Report, reanalysis from the National Center for Environmental Prediction (NCEP), and the Asian Precipitation Highly Resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE) project data as the observation. SDSM and the focused time-delayed neural network (TDNN) models are used for the downscaling. The projected annual increase for Representative Concentration Pathway (RCP) is 8.5; the average temperature is 2.83 °C (SDSM) and 3.03 °C (TDNN), and rainfall is 33% (SDSM) and 63% (TDNN) for 2080’s.