At present, soil pollution through heavy metals occupies a higher percentage worldwide.Conventional methods of soil heavy metal estimation are very much laborious, time consuming and tedious to cover large scale area.Hence, rapid, robust and reliable method for heavy metal estimation in soil is an important step in agricultural and natural resource management.With the advancement in digital science, remote sensing with geographical information system acts as a new tool to obtain both qualitative and quantitative information on soil in a non-invasive, cost-effective, and environmentally friendly manner in large areas.This review emphasis of using digital spectroscopy and other freely available satellite sources for the assessment of soil pollutants.Remote sensing is the best alternative tool for mapping soil pollutants and more precise delineation of polluted areas.However, this information can be very much helpful for the policy makers and planners for planning the site specific management strategies.

Heterogeneous traffic flow significantly influences the noise level in the surrounding area of highways.The traffic composition in India comprises vehicles with different sizes, speeds, and operating characteristics.Noise pollution is recognized as a major environmental stressor affecting human health.This study predicts traffic noise using traffic data from National Highways with free-flow traffic conditions.A radar gun measures vehicular speed, and videography documents traffic volume.Furthermore, the octave spectrum characteristics of traffic acoustics were analyzed.A number of 460 data sets were meticulously compiled at 15minute intervals across weekdays and weekends, encompassing peak and off-peak periods.This data was used to predict equivalent noise levels (LAeq) using multiple regression techniques.The developed model is compared with the Calculation of Road Traffic Noise (CORTN) model and other recent empirical models.A comparative significance of the input variables is evaluated.Heavy vehicles are observed as the most significant variable, followed by speed.Six scenarios were developed to predict the traffic noise.The results of this study will help to predict and reduce traffic noise in mixed traffic flow conditions.

Contemporary multi-family architecture is shaped by formal and legal regulations, evolving demographics, and the imperative to adapt to climate change.Developers are increasingly challenged to meet these diverse requirements while addressing the needs of various social groups.This paper discusses the factors that influence modern residential architecture.The main objective of the research is to determine the impact of developer-driven architecture on environmentally friendly solutions in multi-family buildings.This study aims to bridge a research gap by exploring the interplay between multi-family housing, environmentally friendly solutions, and the influence of developers on architectural design.A comparative and critical analysis method is employed, informed by the authors' observations, professional design experience, and a review of relevant literature.Four examples of different buildings were used.The specific scope of the subject allowed for a diverse selection of solutions and the ability to assess the range of possibilities and design constraints.The study focuses on selected, significant issues related to multi-family architecture.Key findings highlight the growing role of eco-friendly architecture in the context of multi-family housing in Poland, and contemporary challenges require developers to adopt flexible and innovative design approaches.The results may provide an opportunity for further discussion to deepen the knowledge of architects, urban planners, developers, or lawmakers, and to contribute to the development of more sustainable and user-friendly multi-family architecture.

This study aims to explore how to optimize sports tourism camping sites through ecological landscape planning to provide a place full of natural environment experience.In order to conduct in-depth analysis and calculation, this paper uses Python programming language and related libraries and GIS (geographic information system) software to clean, process, convert and scale the data.Combined with the online computing platform and the regional ecological footprint calculation method, the ecological footprint of the sports tourism camping site was evaluated and analyzed in detail, and the corresponding footprint index and report were generated.Using satellite remote sensing images and DEM data such as Landsat and ASTER, as well as GIS tools such as QGIS, the terrain and landscape of the area where the sports tourism camping site is located were accurately modeled and analyzed.The entropy weight method was used to redistribute the weights of each factor, more accurately depicting the current status of the ecological environment and its future development trend, and providing a scientific basis for the design and construction of sports tourism camping sites.The optimized wastewater discharge ecological footprint index was reduced from 8.8% to 5.8%, verifying the effectiveness and practicality of the ecological landscape planning model of sports tourism camping sites based on ecological footprint.

Air pollution poses a dire global crisis that requires effective management strategies.One of the most promising methods is the utilisation of air quality prediction techniques.Recent studies have demonstrated the potential of artificial neural networks (ANNs) in accurately forecasting air pollutants.To delve deeper into this, a comprehensive study encompassing a 10-year period was conducted, focusing on the study area.The meteorological and pollution parameters for the model development were selected using Principal Component Analysis (PCA) and Cluster Analysis of meteorological and pollution parameters.The Output for PCA consists of a) Descriptive statistics b) Correlation matrix c) Rotated Component matrix.For all the four seasons KMO test sampling adequacy is found to be higher than 0.5, and Significance from Bartlett's test is lesser then 0.00 which implies the analysis is satisfactory.Whereas the output for Cluster analysis consist of Dendrogram, which helps in identifying a meaningful subgroup.For further validity the results of PCA and CA were compared.A total of 108 artificial neural network (ANN) models were developed based on the selection of input data by accuracy and efficiency.These models varied in the number of neurons, with sizes of 10, 15, and 20, each using different sets of data.The research utilized a two-layer neural network model employing the non-linear time series prediction and modelling tool (ntstool).The standard NARX network structure consisted of a two-layer feedforward network with a sigmoid transfer function in the hidden layer and a linear transfer function in the output layer.However, only 21 models demonstrated compatibility with the R and MSE values, with the data producing the minimum error being deemed compatible.Interestingly, it was noted that the MSE and R values showed no significant changes for models with 15 and 20 layers, leading to the decision to halt the modelling process after 20 layers.The study used MATLAB software to analyse the accuracy of model predictions.Three plots were used: Performance, Error histogram, and Response plot.The error histogram indicates positive or negative test results.During summer, certain parameters had positive histograms, indicating lower values than targets.Postmonsoon, CO, NOx, and VOC parameters had positive histograms, while PM and Xyelene parameters had more negative errors.Winter season showed positive histograms for CO, NOx, SO2, Benzene, and Toluene.

Rockfall hazards significantly threaten rocky slopes, often leading to severe consequences.The Cap Oukass cliff in Northeast Algeria is particularly prone to recurrent rockfalls, which have resulted in the loss of human lives.Our research integrates probabilistic, structural, and photogrammetric techniques to comprehensively evaluate the rockfall risk in this area and identify contributing factors.The adopted methodology combines kinematic analysis and the Matterock method to pinpoint zones susceptible to rockfall mobilization.Additionally, we conduct 3D trajectory simulations to understand the behavior of individual rockfall blocks.A key aspect of our study is the examination of fault sets and their influence on the cliff's instability.Using the energy-return period couple, we generate a hazard map categorizing the RN9 road as within the medium to high hazard range.Our qualitative assessment of rockfall risk considers various predisposing and triggering factors.The findings of this study not only provide valuable insights into the rockfall risk at Cap Oukass cliff and suggest effective mitigation strategies.Furthermore, our approach can be extrapolated to assess and categorize rockfall hazards and risks in other rocky slopes with similar characteristics.These results contribute to developing effective management strategies to mitigate the associated risks of rockfall hazards.

Flash flooding represents a considerable threat in semi-arid and arid areas, especially where geomorphological and hydrological factors maximize the impact of extreme rainfall.The research evaluates flash flood risk in the Zarqa Main Basin (ZMB) of central Jordan, emphasizing the roles of topography, land use, and geology in flood susceptibility.The study utilizes Geographic Information System (GIS) methodologies combined with hydrological modeling in the delineation of areas at greater risk, utilizing the 2018 flash flood occurrences as case studies.On October 25, 2018, an unusual precipitation event (43 mm in 22 minutes) resulted in widespread flooding culminating in 21 deaths.A second flooding event took place on November 10 in southern Jordan and resulted in fatalities.Although recent heavy precipitation events are interpreted within the context of larger climatic fluctuations, climate model projections and trend analyses over the long term are not included in this study.RCPs and vulnerability indices should be included in future studies to more effectively interpret climate change implications for flood regime in the region.The study informs an understanding of geomorphological and hydrological controls' interactions under the present climatic regime and thereby informs risk reduction measures in similar arid basins.

Excess nitrogen oxides (NOx) emissions lead to severe environmental problems such as acid rain, photochemical smog, and ozone layer depletion, while also contributing to PM2.5 and O3 formation, posing risks to human health and ecosystems.Among available technologies, ammonia selective catalytic reduction (NH3-SCR) is the most effective approach for NOx control, with catalyst performance determining its overall efficiency.This paper reviews the NH3-SCR denitrification mechanism, the impact of sulfur dioxide (SO2) poisoning, and the interrelationship between SO2 deactivation and catalyst activity.Emphasis is placed on advances in the design and development of manganese-cerium-based catalysts with enhanced sulfur resistance, recognized for their high low-temperature activity and potential for industrial application.Recent studies are summarized from three perspectives: elucidation of the denitrification and SO2 poisoning mechanisms; strategies to inhibit ammonium bisulfate (ABS) formation on catalyst surfaces; and improvements in sulfur resistance achieved through metal element doping (Fe, Cu, Pr, Co, Ni, Sn, Ti), optimized preparation methods (co-precipitation, hydrothermal, sol-gel), and structural innovations (core-shell and hollow architectures).These developments collectively enhance redox performance, oxygen storage capacity, and acid site availability, improving both activity and stability under SO2-rich conditions.The review concludes that while manganese-cerium-based catalysts exhibit promising denitrification performance at low temperatures, their long-term industrial use remains limited by incomplete sulfur resistance and deactivation recovery.Continued research is required to refine catalyst structures, elucidate poisoning mechanisms, and integrate protective designs that ensure durability and cost-effectiveness in industrial flue gas applications.This work provides insights into future directions for the rational design of sulfur-tolerant, low-temperature NH3-SCR catalysts aimed at sustainable air pollution control.