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Groundwater remains the primary source of freshwater for essential agricultural practice. Two billion people depend on groundwater as their essential source of water. Thus, fifty percent of the groundwater is used to produce food for the globe. Groundwater serves as the main strategic asset in periods of drought. Determining the groundwater potential zones highly facilitates the effective management of available water resources. The current study attempts to find out the potential zones for groundwater using remote sensing and geographical information system. This tool has become deliberate for groundwater potentiality modeling and mapping as it is highly efficient, accurate, and cost-effective. Satellite and conventional data sources include a total of nine parameters, such as rainfall, geology, geomorphology, lineament density, drainage density, soil, land use, slope, and topographic wetness index, which are considered for the identification of groundwater potential by using multicriteria decision-making approaches such as analytical hierarchy process (AHP), frequency ratio (FR), and multi-influencing factor (MIF). The study is applied to Tanjore District where the proposed methodology has not been attempted in earlier studies. The potential groundwater zones obtained were categorized into five classes: very low, low, moderate, high, and very high. The receiver operating characteristic curve was plotted, and the area under the curve was estimated for results, and the precision showed FR 88%, AHP 75%, and MIF 71%. The results will be valuable for controlling water supplies, land-use planning, and environmental management purposes for planners and engineers.

Plastic pollution is one of the major problems being faced by the world today. The lack of proper recycling and insufficient waste management practices; leaves more than 55% of plastic waste unattended resulting in open dumping of plastic waste. Microplastic pollution is one of the niche areas of study addressed by a few researchers and inventors. Researchers are working on various aspects concerned with the microplastic impact on marine, freshwater, and soil environment. The paper covers a detailed patent landscape for analysis, extraction, identification, and separation techniques for microplastics in marine, freshwater, and soil ecosystem. The patent analysis is performed through the IP classification, applicants, and inventor’s details, citation analysis, patent grant, kind codes, country codes, document types, and jurisdictions. The paper also includes relevant bibliometric analysis of the research publications in the area of microplastics identification, separation, and analysis techniques supporting the patent landscape to understand the overall research scenario in the area of microplastic pollution. The patent landscape is done by using the Lens patent database, Espacenet, and applications such as MS excel and origin. The objective of this study is to provide an overview of the research trends and the need for research and innovation in the field of microplastics pollution, considering the patenting activity and publications as an indicator of innovation.

Air pollution is one of the main environmental problems in the metropolitan area of Sao Paulo (MASP) in Brazil, with frequent exceedances of air quality standards. Occasionally, the exceedance events last many days, resulting in continuous exposure to concentrations above the standards, with impacts to human health. In this air pollution long-term study, a method was developed to identify persistent exceedance events (PEE) of particulate matter (PM10) and ozone (O3) and associated surface weather patterns. Between 2005 and 2017, 119 PEE were identified, with exceedances occurring simultaneously in at least 50% of monitoring stations along 3 to 14 consecutive days. Median PM10 and O3 concentrations increased by 60% during the events. Mean fields of sea level pressure from global reanalysis data revealed the influence of high-pressure systems and pre-frontal conditions. PM10 events were frequent in austral winter and mostly driven by anomalous atmospheric circulation with a wind change to northwest. On the other hand, O3 events were common in the spring, associated with higher positive anomalies of temperature and solar radiation. Overall, results show that PEE span a regional scale, differing from ordinary exceedance events that can be driven by local conditions. Policy makers should be aware of the frequency of PEE in the development of mitigation measures against the exposure to harmful levels of air pollutants. The concept of PEE can be applied to other metropolitan areas and may support the development of data-driven air quality predictive models based on current or forecasted weather conditions.

Coronavirus disease 2019 brings about the economic damage and loss of life. Thus, demand of personal protective equipment continues to increase, consequently an increase in infectious equipment pollution. Most of these wastes threaten the environment and increase the spread of diseases. This paper provides a research hypothesis whether effective medical waste management would prevent the possible impacts of coronavirus disease 2019-related waste issues on environment at the city level. To confirm this hypothesis, installation of waste treatment centre is addressed. Then, by incorporating uncertain waste generation amounts utilizing Jimenez method, a pickup routing is addressed to decide the pickup routes between the waste treatment centre and residential area. This study is first to assign the optimistic, realistic and pessimistic scenarios of the uncertain waste generation using time series analysis method and waste generation formulation. Also, L-type matrix is used to define, assess and prioritize the environmental and operational risks on waste generation formulation and to provide risk reaction strategies. Practicality of these approaches is illustrated in the case of Turkey. The computational results reveal the effectiveness of the integrated method, which ensures practical and theoretical insights controlling the waste generation to prevent virus propagation for health authorities.

The realization of the goals of sustainable development (SDGs) presupposes administrative policy capacity. The NPM implementation is related to that capacity. Empirical research on this relationship has been conducted rarely in developing countries. To fill this gap, this research aims to study the impact of the policy capacity on the implementation of one of the NMP themes as well as a method to achieve SDGs, namely, performance-based budgeting (PBB) and the impact of PBB on increasing the policy capacity. Iran has been selected for a case study and the data from interviews as well as documents, news, and related scientific papers have been analyzed by a qualitative method. The results showed that 1) one of the requirements for realizing the SDGs is administrative policy capacity. 2) the lack of that capacity caused many problems for the implementation of the PBB by preventing the formulation of the budgeting system change plan and the appropriate annual plan. And, 3) the implementation of PBB did not increase the policy capacity.

Emission control strategies, meteorological factors, and interregional atmospheric transport of pollutants are considered significant factors that affect PM2.5 concentrations in a city. In this research, two multiple linear regression models are proposed to achieve an accurate estimation of PM2.5 concentrations in Zibo City, Shandong Province of China. The influence of policy was considered as dummy variables in the models. First, a PM2.5–meteorological–policy–interregional model was constructed to explore the diversity of influences from all the parameters mentioned above. Second, for easy predictions of PM2.5 concentrations in the future, a PM2.5–meteorological–policy model was constructed considering meteorological factors from ground monitoring and pertinent air control policies. Data needed to construct the models were obtained from the monthly statistical data from Zibo and surrounding cities from January 2014 to September 2019. Then, the model predictions were extended to July 2021 and compared with the actual series. Model verification was revealed using goodness-of-fit and significant testing statistics. In specific, temperature, wind velocity, and the implementation of relative policies had clear effects on the PM2.5 concentrations in Zibo. Furthermore, Jinan, the provincial capital bordering Zibo to the west, made the largest contribution to fluctuations in PM2.5 concentrations in Zibo. Accordingly, the implementation of strict policies and regulations was undoubtedly a better measure to reduce PM2.5 concentrations in a city under certain topographic and climatic conditions.

One of the ways to implement practical plans in planning and protecting them is to be aware of the ecological potential of these resources and to investigate the factors affecting their degradation. In this Hyrcanian forest study in northern Iran, a study was conducted to determine the ecological capability of the forest based on three methods: AHP, fuzzy gamma and regression based on GIS and weighting of factors with AHP model. Then, using field data and stepwise regression model, a region decline map was constructed. The validation results of the three methods using the ROC model showed that the fuzzy gamma estimates the region capability with the highest accuracy and only 27% of the region has high and very high capability. Livestock grazing, climate change, land use change and diseases have played the most important role in the decline. The decline map developed along with the capability map can be helpful to any management of oak forests in the area to reduce decline factors and prioritize areas for conservation and support programs.

Due to increased awareness of the current and future issues with the environment, sustainable development has become a well-known concept and goal in the last decades. Based on this idea, organic agriculture is now fairly generalized in many farms and grape processing factories. Life cycle assessment (LCA) represents a valuable and standardized technique to evaluate how sustainable a crop can become, determining the potential impacts that the complete-life product causes on the environment and on the management/conservation of resources. Although LCA can be applied to any product, this work focusses on organic grapevine crops as the subject of study, in order to improve energy and water efficiency and minimizing issues such as the use of pesticides. This paper, collecting primary data from three Spanish grape processing factories, quantifies the reduction of the overall impact related to the avoidance of artificial irrigation, amounting to 10%, and the saving of resources (− 4.3 kg oil eq. per ha) due to the replacement of chemical fertilisers with animal manure. On the other hand, the use of manure has shown some controversy from an environmental point of view as it contributes to global warming, resulting in an increase in the total impact of the organic vineyard. As predictable, the application of conventional practices as well as the use of water for irrigation could both result in a higher overall yield, and in a contemporary growth of environmental impacts; a discussion about the quantification of this aspect is also inserted.

Enhancing the construction of transportation infrastructure is considered one of the viable pathways to ease traffic congestion and air pollution problems. To investigate whether increasing the level of transportation infrastructure construction can help reduce haze problem, a random-effects model is used to test the effects of haze reduction, based on inter-provincial panel data of 31 Chinese provinces from 2013 to 2017. The empirical results deliver several interesting findings. Firstly, enhancing transportation infrastructure construction has a statistically significant and positive impact on haze emission reduction; secondly, upgrading the industrial structure enhances this optimistic effect. Finally, improving transportation infrastructure construction shows more significant effects in haze reduction within highly polluted areas than lightly polluted cities. Therefore, to reduce haze pollution more effectively, China needs to improve the level of transportation infrastructure construction further and adopt a differentiated haze management strategy for high and low pollution areas.

Drainage basins act as indicators of geomorphic information for hydrogeological and sustainable natural resources development. Concerning the impacts of short-duration excessive downpour and flash floods worldwide, management strategies for such analysis accordingly assume a paramount importance. Understanding drainage characteristics of watersheds for stability and gradual release of waters downstream is imperative to recognize their linkage to erosion and flash flood processes. This study proposed an empirical and semiquantitative method for determining an index referred herein as erosion and flash flood susceptibility index for 18 major stream drainage areas of the Jhelum River Basin (JRB) of Kashmir Himalaya, India. The index was employed to assess and prioritize stream watersheds, considering statistically driven weightage-based influence of 23 morphometric attributes and 10 slope categories which govern the erosion and flash floods of these watersheds. Results demonstrated that about 83% of the JRB, encompassing 12 stream watersheds, fall under high- to moderate-priority classes and thereby contribute to frequent flooding and erosion in the eventuality of heavy downpours. The new index provided in this research would help in better understanding stream watershed conditions and deliver recommendations for watershed management authorities in regions deficient in hydrometric and erosion yield datasets. The present work further would support the regions to accomplish various sustainable development goals after implementing the management procedures. The findings arising out of this study will provide tangible inputs for watershed management authorities in the region typically characterized as deficient in hydrometric and erosion yield datasets.

Hydroelectric generation is a potential flexible electricity source that can ease the transition to a decarbonized energy economy. As such, using scarce hydroelectric generating resources efficiently is important. We examine approaches to represent the operating characteristics of hydroelectric resources. Many hydroelectric-plant owners use water tables or generic unit characteristics for operational planning. Such practice may be inefficient, as it does not account for unit-specific operating-characteristic changes or time-related impacts, e.g., plant degradation. We demonstrate a data-driven approach to modeling plant operations that is unit-specific and depends solely on observable and controllable variables. Numerical results using historical data for four hydroelectric units illustrate the proposed methodology.

The prime purpose of this study is to explore the use of RS and GIS for flood risk mapping in the Patna district in conjunction with the frequency ratio and entropy model. The variables such as elevation, LULC, rainfall, slope, distance from the river, TWI, and drainage density have played significant roles in causing floods in the study area. The flood vulnerability map has been generated and categorized as very high, high, moderate, low, and very low vulnerable flood areas. The validation of the results outcomes was conducted using the ROC curve, which indicates that the frequency ratio model for flood vulnerability mapping is more authentic, reliable and can assist in demarcating flood vulnerable areas and potential flood risk sites. The results show that about 16.35% (FR model) and 9.98% (SEI model) of the study area have come under very high risk of flooding, especially located in the southeast and northwestern sites of the district. The blockwise flood risk assessment shows that 13 out of 23 blocks recorded over 25% of their total area under very high flood risk. The present study reveals the usefulness of RS and GIS techniques along with bivariate models for the investigation and evaluation of flood vulnerabilities as they provide a wide range of data and reliable platforms. The results of this report will assist planners in developing flood prevention plans as part of regional flood risk management initiatives and will serve as a foundation for future studies in the study region.

Iran law makes municipalities responsible for recycling or disposing of household waste. Therefore, municipalities can use the geographical and cultural potential of each area as an opportunity to improve the situation. However, waste management would be impossible without people's participation. In Iran, neither has much effort been made to increase people's participation, nor have appropriate policies been implemented to increase the amount of recycling at source. In this study, an attempt was made to get citizens to participate voluntarily in household waste management for six months. They were encouraged to dry their food waste. The results of this study showed that due to the hot and arid climate of Iran and the pattern of Iranian food consumption, dehydration of food waste at the source of production can reduce their mass by a maximum of 80%, and as a result, the cost and frequency of collecting them will drop drastically. Moreover, implementing this method not only removed leachate but also increased people's responsibility for the produced waste. Therefore, one can conclude if people participated, Iran's geographical and climatic circumstances would allow food waste to dry at the source by imposing the lowest cost and energy consumption on households. In that case, the costs of collecting, disposing, managing of waste and leachate, as well as the environmental damage will reduce significantly. Also, waste separation will be done better because households have to separate their food waste from dry and recyclable waste.

Surface Urban Heat Islands significantly deteriorate urban sustainability and livability. Effective ways to monitor and mitigate this phenomenon are gaining importance among environment researchers. In order to lessen the extreme heat waves of tropical cities, an understanding of the spatial features affecting urban micro-climate is needed. Local climate zones can provide the much needed framework for city level climate studies. This paper presented satellite imagery-based workflow to identify critical zones within a city, using the spatial thermal characteristics. The results brought out the critical thermal scenario in summer, for two mega cities of India. Prolonged heat trapping resulted in 13.99% nocturnal heat island cover in the New Delhi region and 22.26% in the Kolkata City region. Moderate to strong heat stress was observed for central city areas, even at night time. Surface temperature variation within local climate zones of a city reflected significant rise in temperature over certain urban pockets due to building morphology. Over a distance of 12 km from vegetated land to low-rise dense built-up, a drastic 8.76 °C temperature rise was noted on a summer night. The temperature patterns were further verified with seasonal surface albedo mapping. Based on areal distribution, location, thermal range and contribution in heat island formation, sustainable planning strategies for each local climate zone were laid out. The study aimed to achieve an optimum cooling level for given urban structures and also highlighted the potential thermal environment changes faced by suburban regions.

This study aims to analyze the pattern of bank erosion and simulate the physical aspects of vulnerability in the lower Meghna River, Bangladesh using remote sensing (RS) and geographic information systems (GIS). The physical factors of vulnerability were analyzed using GIS-based Structured Query Language (SQL). A questionnaire survey, GPS survey and field observation survey were conducted for collecting the primary data in the study area. The secondary data were mainly satellite image collected from the United States Geological Survey (USGS) website. Using time series Landsat images (MSS, TM and OLI-TIRS), this study analyzed 36 years of erosion and accretion process in the Mehendiganj Upazila region from 1980 to 2016. The result indicates that an enormous amount of land (4470.47 ha) was submerged by the river and average land loss rate was 124.18 ha/year. The study quantifies the number of vulnerable households beneath the present condition and how much it will be altered after a positive/negative change with the factors of vulnerability related to the households. Simulation data reveals that under the present physical condition, 43.88% of households were identified as severely vulnerable. The output of this study can be used in the classification of vulnerable households and for the improvement of the physical infrastructure development process near the erosion prone areas, also helps to mitigate environmental disaster in the developing countries.

The goal of this study was to assess the impact of the introduction of various waste management methods on the amount of greenhouse gas emissions from these activities. The assessment was carried out on the example of the Russian waste management sector. For this purpose, three scenarios had been elaborated for the development of the Russian waste management sector: Basic scenario, Reactive scenario and Innovative scenario. For each of the scenarios, the amount of greenhouse gas emissions generated during waste management was calculated. The calculation was based on the 2006 Intergovernmental Panel on Climate Change Guidelines for National Greenhouse Gas Inventories. The results of the greenhouse gas net emissions calculation are as follows: 64 Mt CO2-eq./a for the basic scenario, 12.8 Mt CO2-eq./a for the reactive scenario, and 3.7 Mt CO2-eq./a for the innovative scenario. An assessment was made of the impact of the introduction of various waste treatment technologies on the amounts of greenhouse gas emissions generated in the waste management sector. An important factor influencing the reduction in greenhouse gas emissions from landfills is the recovery and thermal utilization of 60% of the generated landfill gas. The introduction of a separate collection system that allows to separately collect 20% of the total amount of generated municipal solid waste along with twofold increase in the share of incinerated waste leads to a more than threefold reduction in total greenhouse gas emissions from the waste management sector.

Rapid urbanization and poor management of solid waste in cities have escalated to unfavorable environmental impacts. This paper uses blockchain technology to propose a smart waste management system for mitigating the damaging impacts of traditional waste management services. This contributes to a healthy environment by optimizing the storage, collection, and disposal of solid waste. Blockchain, though in its budding stage, is believed to have a transformative impact on the world economy by enabling near real-time agreement of any transactions in a peer-to-peer and distributed manner. The growth of smart waste management systems using the Internet of Things (IoT) has been prevalent, but the use of blockchain in this application area is almost negligible. This paper identifies the problems associated with the traditional waste management system, defines algorithms that underpin the proposed smart waste management system, and compares the two systems by discussing how the latter alleviates the problems of the traditional waste management system. This paper utilizes smart contracts on the Ethereum blockchain to design a proof of concept of the proposed smart waste management system, and hence, the system can be measured at Technology Readiness Level 4 (TRL 4). This paper also calculates the cost of deploying the smart contracts onto the Ethereum private test net.

The purpose of this study was to determine possible damage created by recreational activities in Yedigoller (Seven Lakes) National Park and to recommend recreational impact management decisions in order to ensure its sustainable use. Four picnic and camping areas and one control area in the National Park were selected as sampling areas. In order to determine the ecological effects of recreational activities and the pressures on the plant life and the soil, an observation form was used. The study also addressed the stress on the Fagus orientalis trees in all recreation areas near Nazli Lake, Buyuk Lake, and Derin Lake. Findings from the soil analysis indicated that in general, intense human activities in the area had negatively affected both the physical and chemical properties of the soil. As a result of the evaluation of the observation forms, all of the sampling areas in the park were determined to be in poor condition. project titled Research on Possible Damage of Recreational Activities in Yedigoller National Park This study was supported by the project titled Research on Possible Damage of Recreational Activities in Yedigoller National Park upon the request of the Nature Conservation and National Parks 9th Regional Directorate of the Bolu National Parks Branch Directorate. We also thank Assoc. Prof. Dr. Hulya Torun for contributing to the measurement of the physiological parameters in the study. WOS:000674564600002 2-s2.0-85110806711

The design of an optimum hydrometeorological and hydrometric station network constitutes a key factor for the collection of comprehensive and reliable hydrometeorological and flow data that are necessary both for decision making in water resources policy and management, and for the hydrometeorological risk assessment. This article describes a methodology developed in a geographic information system (GIS) using a multicriteria decision making (MCDM) approach, which combines several spatial criteria to propose suitable locations for installation of such a station network in the Sarantapotamos River Basin in the western part of the Attica Region, Greece. Through the design of two networks that meet different requirements, various aspects concerning this methodology are illustrated, such as criteria weights determination, which is a problem that arises frequently in many MCDM techniques. The criteria weights for the hydrometric station network design are estimated using both the Analytic Hierarchy Process (AHP) and the Fuzzy Analytic Hierarchy Process (FAHP), while, for hydrometeorological station network design, all weights are equal. Hydrometeorological station final position selection is achieved by introducing the criteria of density and spatial distribution to the suitable locations. For hydrometric station network design, the analysis indicates that the criterion of slope mainly controls the MCDM outputs. According to station density thresholds proposed by the World Meteorological Organization (WMO), an optimum hydrometeorological and hydrometric station network for the region should comprise three and two stations, respectively.

Soil erosion is a danger that threatens the world today and the basis of the fight against erosion must be sought in the role human. The aim of this study is determine a logical relationship between natural and planted forests conditions with soil erosion risk classes in the kasilian watershed. This basin is located in the Hyrcanian vegetation area on the northern slopes of the Alborz Mountains in northern Iran. In this research, the erosion risk map was prepared using the ICONA model and RS/GIS techniques and it was adapted to the physical realities of the area. The results showed that human interventions and pressures have reduced habitat good species percentage in the downstream areas in the northern part and upstream areas near the forest-rangeland boundary in the southern part. Also, the choice of species was incorrect in some planted forest. Therefore, high erosion risk class is clearly seen in these areas. There is a low erosion risk class (19.3%) in natural forest and a very low erosion risk class (2.73%) in plantation forest. The main reason for the high percentage of very low erosion risk class in planted forests can be due to the presence of 70–80% of canopy, which is a combination of 90% of broadleaf plants with 10% of conifers. These results are consistent with the realities in the study area. The ICONA model and RS/GIS techniques can be used as a reliable framework for erosion risk assessment.

Droughts can cause devastating impacts on water and land resources and therefore monitoring these events forms an integral part of planning. The most common approach for detecting drought events and assessing their intensity is use of the Standardized Precipitation Index (SPI), which requires abundant precipitation records at good spatial distribution. This may restrict SPI usage in many regions around the world, particularly in areas with limited numbers of ground meteorological stations. Therefore, the use of remotely sensed derived data of precipitation can contribute to drought monitoring. In this study, remotely sensed precipitation estimates from the POWER/Agroclimatology archive of NASA and their derived SPI for different time intervals were evaluated against gauged observations of precipitation from 13 different stations in arid and semiarid locations in Jordan. Results showed significant correlations between remotely sensed and ground data with relatively high R values (0.67–0.91), particularly where seasonal precipitation exceeded 50 mm/year. For evaluation of remotely sensed data in SPI calculation, several objective functions were used; the results showed that SPI based on satellite estimates (SAT-SPI) showed good performance in detecting extreme droughts and indicating wet/dry conditions. However, SAT-SPI showed high tendency to overestimate drought intensity. Based on these findings, remotely sensed precipitation from the POWER/Agroclimatology archive showed good potential for use in detecting extreme meteorological drought with the provision of careful interpretation of the data. These types of studies are essential for evaluating the applicability of new drought monitoring information and tools to support decision-making at relevant scales.

Anthropogenically influenced transboundary catchment areas require an appropriately adapted exposure modelling. In such catchments, water management decisions strongly influence and override natural river hydrology. We adapted the existing exposure assessment model GREAT-ER to better represent artificially overprinted hydrological conditions in the simulations. Changes in flow directions and emission routes depending on boundary conditions can be taken into account by the adopted approach. The approach was applied in a case study for the drug metformin in the cross-border catchment of Vecht (Germany/Netherlands). In the Dutch part, pumps to maintain necessary water levels and minimum flow rates during dry periods lead to a reversal of the (natural) flow directions and as a consequence to additional pollutant input from the Lower Rhine/Ijssel along with a spatial redistribution of emissions in the catchment area. The model results for the pharmaceutical product metformin show plausible concentration patterns that are consistent with both monitoring results and literature findings at mean discharges and the effects of the changed hydrology in times of low natural discharges, namely an increase in polluted river sections under dry conditions due to the pumping activities. The adapted methodology allows for realistic application of the GREAT-ER model in anthropogenically modified catchments. The approach can be used in similar catchments worldwide for more realistic aquatic exposure assessment.

E-waste is the globally mounting solid waste stream which is increasing due to domestic as well as a result of transboundary exportation. This solid waste stream is considered as an amalgam of valuable and toxic material which is concern of environmental and human health risks. To accomplish successful management of electronic equipment after post-consumer stage, numerous lucid initiatives have been offered globally. The ever-increasing quantum of e-waste has enforced environmental organizations of many developing world such as India and China to invent, and implement environmentally favorable opportunities and strategies for mitigation and control of environmental and human threat. Many developed countries have given topmost priorities for e-waste management, so setup appropriate facilities to treat maximum of the generated e-waste, but in low economic nation like India, economic conditions, infrastructure scarcity, and inappropriate legislations are some of the problematic issues which dodge complete replication of the WEEEs management system. Our study represents an evaluation and valuation, of e-waste generation and cross-border movement to the management strategies followed in highly industrialized as well as developing nations. These findings would expand into regulated and non-regulated countries to put more emphasize on e-waste management.

The role of technological innovation in lowering the pollution is motivated by the rise of global emissions. Technological innovation for carbon dioxide mitigation mainly focuses on the role of patents ignoring trademarks. This study tests the role of eco-patents and trademarks for carbon dioxide alleviation using data from 2000 to 2018. This study introduces a new interaction term by combining trademarks and eco-patents and examining the role of environmental policy, energy consumption, environmental taxes, urbanization, and economic growth on the environment. The panel effect is tested using the Nonlinear ARDL and OLS methods, whereas country-specific results are estimated using the Granger causality approach. The outcomes reveal that trademarks and eco-patents decrease carbon dioxide emission; however, energy consumption, urbanization and economic growth are the main polluters in both regions. Environmental policies and taxes have a significant impact on the western region’s mitigation efforts, while insignificant evidence is found for the southern region. Unidirectional and bidirectional causal relationships are observed among the variables; still, the outcomes are country-specific. The study also provides policy implications based on the results.

Aviation has the most intense impact on air quality during the landing and takeoff cycles (LTO) of aircraft operations, which are defined as being equivalent by aircraft manufacturers, airport managers and regulators. In this study, an environmental impact assessment and cost analysis study was conducted, taking into consideration airline traffic data from 2018 of Turkey's biggest commercial airline company. Calculations are based on the LTO cycle. Emissions evaluated in the study are CO, CO2, HC and NOx. In order to assess the emissions, the aircraft types in which the airline forms the fleet, tail numbers, engine types, number of seats, passenger-carrying capacity, emission indices at different power settings of the engines and annual passenger traffic of the aircraft have been employed. In addition, two approaches are employed to explain the meaning of emissions: the amount of environmental impact and the environmental damage cost. In the light of data generated by calculating the annual environmental costs of the fleets, fleet renewal or employment of new engine technologies can be caused to low-emission tariffs that achieve more efficient combustion. As a result, changing the engine combinations of aircraft in the Turkish Airlines fleet will result in fewer emissions and therefore less cost. The important thing is the optimization of the ratio of the cost of environmental impact on the cost of changes in the combination of aircraft engines.

Like other large-scale development projects, the design, construction, and utilization of subway projects have several positive and negative environmental consequences. In this study, geological hazards in the construction phase of Ahvaz Subway Line 1identified and localized. The environmental impacts also were assessed in the surface and subsurface sections. During the surface evaluation, after providing and preparing the required information layers, AHP and TOPSIS methods were used to model the environmental effects in the subway route. During the subsurface evaluation, the subsidence, liquefaction, soil corrosion, and RQD characteristics were examined separately in a vertical section. The results showed that Stations 1, 2, 11, and 12 had the most significant potential for environmental hazards at the surface section. Assessing the subsurface conditions showed that due to the type of alluvial material and lithology and water table, subsidence was possible in a subway route. Due to the region's seismic conditions, seismic stresses can cause soil displacement and tunnel collapse along the subway route, especially during the construction phase. The soil chemical test results showed that soils in the subway route had moderate to severe corrosion potential.

Climate change is strongly affecting the Himalayas. Geologically young and fragile, the Himalayas are sensitive to even minor changes in the climate. Regional warming in Himalayas has been observed between 0.15 and 0.60°C per decade, which is higher than the mean global warming rate of 0.74°C per 100years. Consequent to this warming trend, the glaciological changes in Himalayas are obvious, which has resulted in the formation, expansion and disappearance of various types of glacial lakes. The dangerous lakes among these pose threat to downstream community and infrastructure. In this study, an attempt has been made to critically analyze the progress in Glacier Lake Outburst Floods (GLOF) research and understand its dynamism from multiple approaches through a meta-analysis of peer-reviewed scholarly literature for a period of 2001–2020. The study has found that the glacial lakes in the study region are increasing in number and expanding rapidly owing to the climate change and anthropogenic causes as the Glacier Lake Outburst Floods (GLOFs) are becoming common. The expansion rates of pro-glacial lakes connected to glaciers and moraine-dammed lakes are faster than other type of lakes. Findings from the studies on area change analysis in the region reveal that a number of expanding glacial lakes can emerge as potential sites for future GLOFs, hence need immediate monitoring and observation. Geospatial tools and techniques coupled with field investigations have been found as a potent tool for mapping the evolution and propagation of GLOF hazards resulting from accelerated shrinking and thinning of glaciers and continued lake expansion. Further, the satellite-based remote sensing and modeling has been found as an excellent tool for GLOF management as well as reconstruction of previous GLOF events and prediction of future outburst potential. The idea of this study is linked to the increased incidence of GLOF events in the Himalayan region. This study will help in better understanding of glacial lake expansion and provide scope for future research in devising risk management action plans of potential GLOFs by selecting expanding glacial lakes as case studies.

Flood-control infrastructures—channelization, levees, and dams—are considered as the flood mitigation which can control flood disturbances of rivers. However, the flood mitigation, which is deemed as a rigid and one-dimensional approach, not only increases the frequency and severity of floods but also destroys the ecological and societal aspects of rivers. The Kan River, in Tehran, is one of the rivers which is equipped by channels, but it is still considered as one of the chaotic rivers in Tehran. Therefore, an alternative plan instead of channelization is required in order to mitigate the floods of the river. In this regard, we seek to propose an indigenous and flood-friendly plan for the river against flood disturbances. To this purpose, the theoretical foundation of social-ecological resilience is considered as an appropriate approach; thus, the theory of social-ecological resilience is reviewed, and its indicators are extracted. Then, based on the inherent characteristics and landscape of the river, a resilient plan in the face of floods is proposed. We argue that the river should be considered as a dynamic and social-ecological phenomenon. Such a view would help to overcome the disturbances of the river and avoid the one-dimensional planning and approaches. In addition, we suggest that the indicators of social-ecological resilience can be utilized to provide and suggest large-scale, middle-scale, and micro-scale strategies in order to improve the resiliency of the river against floods. The findings of this paper can be applied to the rehabilitation of chaotic urban rivers globally.

Purpose of ReviewAs the application space for energy storage systems (ESS) grows, it is crucial to valuate the technical and economic benefits of ESS deployments. Since there are many analytical tools in this space, this paper provides a review of these tools to help the audience find the proper tools for their energy storage analyses.Recent FindingsThere are many software tools for valuating ESS. These tools can be classified into two groups: (1) power system simulation and planning tools for analyzing the technical contributions of ESSs, and (2) techno-economic analysis tools for valuating the economic benefits of ESS deployment and specifying the optimal design of energy systems that include ESSs. While many of the tools, developed by the national laboratories, are free to use, the commercial tools are also of great importance in meeting the customers’ specific needs.SummaryThis paper provides a review of software tools for ESS valuation and design. A review of analysis tools for evaluating the technical impacts of energy storage deployments is also provided, as well as a discussion of development trends for valuation and design tools.

Variability in precipitation results flooding and drought worldwide. The climate change has a close relationship with the precipitation and temperature pattern as well as river discharge. In this research, spatial and temporal rainfall variability have been analyzed. The rainfall data of seventeen meteorological stations for the period of 1980–2018 was acquired from China Meteorological Administration. To assess drought in the study area, the standardized precipitation index (SPI) was applied and to check the temporal drought trend the Mann–Kendall test was applied, while the data normality was checked through skewness, kurtosis, and standard deviation. The Mann–Kendall results shows decreasing rainfall and increasing drought trend in the study area. The study area southwest Xinjiang is an arid region and the decreasing rainfall trend will further aggravate the conditions of already endangered flora and fauna in the region. This study will provide a platform for decision makers to take proactive actions to mitigate the risk of increasing drought in the study area.

First of all, this paper which is based on the panel data of 30 provinces, 27 manufacturing industries, and 1164 listed manufacturing companies in China measures the technological innovation efficiency of the manufacturing industry and then establishes the dynamic system generalized method of moments model and panel threshold model from the regional, industrial and enterprise levels to analyze the impact of environmental regulations on the technological innovation efficiency of China’s manufacturing industry empirically. The results indicate: three kinds of environmental regulation tools have different influences on the technological innovation efficiency of the manufacturing industry and have a certain time lag effect. Overall, environmental regulation has an inhibitory effect on the technological innovation efficiency of the China’s manufacturing industry in the same period. Still, it has a promoting effect on the lag stage. Threshold regression results show that the market incentive environmental regulation tools have a threshold effect on technological innovation efficiency, and there is an inverted "U" type relationship between them. Therefore, China should propose regulation methods according to the environmental pollution situation in different regions, promote enterprises to carry out technological reform, and improve the efficiency of technological innovation.

Globally, the rising level of air pollution is becoming a cause of serious concern. The situation has reached such an alarming situation that knowing the extent of air pollution well in advance has become an absolute necessity before we step out of our home. An advance prediction can help the urban travellers to know the possibility of enhanced pollution ahead of time at strategic locations of a city and thereby be useful in planning a less polluted route. Different cities of the world have pollutants levels with varied dispersion pattern. As a result, a generic prediction model is needed that can cater to all types of pollutants and which can offer better forecasts of pollution data irrespective of their dispersion levels. In this paper, the authors modelled recurrent neural network (RNN)-based bidirectional long short-term memory (Bi-LSTM) that forecasts pollutants concentration with least RMSE. The model is trained and tested on the pollution data of Delhi, the most polluted capital city in the world for the second consecutive year in 2019. Air pollutants like PM10, PM2.5, NO2, O3, and CO are considered that have a varied dispersion pattern. To test the efficacy of our predictions, the model is tested on real data obtained from the Central Pollution Control Board (CPCB) of India. The performance metrics are also generated to evaluate the performance of the proposed model.

Due to its sensitive geopolitical situation as well as giant oil and gas fields, the Persian Gulf has exceptional environmental conditions. However, it is one of the most polluted maritime zones because of strategic sensitivities that have consistently led to political and military tensions, enormous economic resources and marine structures for natural resource extraction. Given the importance of the marine environment, the lack of comprehensive resource monitoring and management and pollution control plan results in environmental degradation. Oil and gas piers are one of the most common offshore structures that may be deteriorated and lead to human and financial losses as well as irreparable environmental impacts. Therefore, damage detection in the early stages of marine structure development is of great importance. The modal strain energy is a non-destructive damage detection method for concrete structures that estimates the damage localization or severity by changing the dynamic properties of the structure. Making some corrections to the previous versions of the modal strain energy method, this study aims to estimate the damage location and severity of a real sample of pile-deck LPG pier in the South Pars Gas Field (Assaluyeh) of Persian Gulf numerically and experimentally. Results showed that the present method has higher accuracy in locating damages than the other methods. Also, single and multiple damages of low and high severity were estimated by this method with good accuracy.

The low-cost sensors and IoT have come to the rescue due to the high cost and operational complexity of equipment and methodologies in environmental monitoring. They are relatively inexpensive and reliable. It is on this assumption that we have decided to use the World Air Quality satellite data supplied by air matters.com. This study is a 40-day preliminary work in which air quality (Air Quality Index (AQI), PM2.5, PM10, NO2, CO, SO2, and O3) and meteorological (temperature, humidity, and wind speed) parameters were monitored. The data collected was for five locations in Lagos State, Nigeria (Ojodu, Opebi, Ikeja, Maryland, and Eti-Osa). The data obtained were subjected to basic statistical analyses. The findings showed that the Opebi had the highest mean value of PM2.5 (69.28 µg/m3), PM10 (107.38 µg/m3), and CO (1392 µg/m3). The mean values of O3 are as follows: 32.52, 38.7, 36.2, 37.85, and 36.13µg/m3 for Ikeja, Maryland, Opebi, Ojodu, and Eti-Osa respectively. Opebi had the highest value (3179µg/m3), followed by Eti-Osa (2978µg/m3), and the lowest value in Maryland (1943µg/m3) among the CO reference locations. AQI of all locations presented the levels of contamination as 'Unhealthy for Vulnerable Groups'. The pollutants were much higher than the World Health Organization (WHO) guidelines. There were relationships between the parameters monitored and meteorological influences, and the effects of natural and man-made activities may be the sources of the elevated pollutants throughout the locations.

This paper studies the shoreline alterations occurring along the Al Batinah region in the Sultanate of Oman and the impacts of Cyclone Kyarr on its shoreline. The methods used in this research employed Sentinel images and the Digital Shoreline Analysis System within the GIS environment to measure changes of the shoreline and explore Kyarr’s impacts from 2017 to 2020. The results showed that from 2017 to 2020, the Al Batinah coast experienced erosion at a rate of 9 m/year. In addition, the Shoreline Change Envelop showed that the maximum shoreline change was reported from 2018 to 2019, with a 1145 m advancement that coincided with Kyarr in 2019. Further, data from the period between 2017 and 2018 showed that 73 % of the coastline was dominated by soil deposition at an average rate of 10 m/year, while the period between 2018 and 2019 showed erosion at an average rate of -40 m/year. The results of this study will be of interest to decision-makers involved in monitoring shoreline changes and would also be of immence importance in ensuring sustainable coastal zone management and development, especially in areas prone to extreme events such as tropical cyclones.

The purpose of the research paper is to investigate the relationship between agricultural development and agricultural energy-driven CO2 emissions. To achieve this purpose, we have taken Bangladesh as the subject of our case study, and appraisal is done by two methods, the Tapio decoupling method, and the logarithmic mean Divisia index method. The result discloses that Bangladesh’s agricultural sector was strongly and weakly decoupled from agricultural sector development in most of the periods between 1990 and 2017. The population factor, the agricultural energy intensity factor, and the agricultural economic activity factor are the responsible factors to increase CO2 emissions. On the other hand, the agricultural economic structure factor, the agricultural energy structure factor, and the agricultural emissions factor are accountable for the decrease of CO2 emissions. Finally, this paper provides some policies to create practical strategies for agricultural CO2 emission reduction. This research paper will help Bangladesh’s agricultural sector to form sustainable economic development.

Wetlands play an important role in the life of the planet. In order to preserve and revive the wetlands, decision-makers need to employ appropriate approaches and tools to identify the main driving forces and this could be an important step toward adopting effective strategies and making critical decisions. The current study used the combined analytic network process with driving forces-pressures-state-impacts-responses framework in mulino decision support system. The Parishan wetland, located in Iran, has been selected as the case study. After an initial study of the conditions of Parishan wetland and identifying the stakeholders, the opinions of experts and local residents were gathered to identify the most important parameters influencing the conditions of the wetland. The analytic network process model was used to improve and reduce the number of input parameters to mulino decision support system. Then, the most important and the best responses associated with the management of Parishan wetland were identified through questionnaires and imported into mulino decision support system along with other components. Finally, the mulino decision support system decision algorithm was run. The findings revealed that reduced precipitation, land-use change, competition of farmers in the wetland to use water resources, and increased temperature were the main driving forces. Moreover, water transfer from the Nargesi Dam was chosen as the most effective response. The results suggest that the driving forces-pressures-state-impacts-responses framework in mulino decision support system algorithm combined with analytic network process model can be used as an integrated tool to study and manage wetland socio-ecological ecosystems.

This study investigates the effects of increasing real energy prices on technical environmental efficiency in Iran using a directional distance function. To achieve this purpose, six variables are used, namely labor, capital stock, energy consumption, real energy price, industrial value added, and environmental cost index, for the period 2009–2015. Moreover, two indexes for industrial environmental cost and real energy price are introduced. The environmental cost index is derived from the pollution cost of six types of emissions (NOx, SO2, CO2, CO, CH4, SPM) produced by 23 industries. The results show that the real energy price and distance function have an inverse relationship. Specifically, higher real energy prices cause industries to move toward using more energy-efficient technologies and more technical environmental efficiency. This study is one of the few studies that have evaluated the environmental effects of targeted subsidy law or energy price liberalization in Iran and demonstrates that an increase in real energy prices would lead to improved environmental technical efficiency.

Management areas in the country are mainly identified and protected to conserve genetic resources, protect natural landscapes and support the origin of ecosystem services. These areas would not be much effective without a proper management plan based on their zoning. Due to lack of appropriate criteria with ground check capability, zoning has always been a major challenge for planners. Therefore, this study was carried out to develop a decision algorithm for zoning protection zones 1 and 2 of Hara protected area. This region, with an area of 86,258 hectares, covers a vast range of Mangrove forests and is located between Khamir Port and Qeshm Island in Hormozgan province, Iran. Zoning of the protected areas in this region was performed through identifying the ecologic-based criteria for Restricted Nature Reserve Zone (zone 1) and Protection Zone (zone 2), finalizing and screening them by Delphi questionnaire and weighting those criteria using Entropy Shannon methods. Moreover, the linear model for spatial criteria combination was developed, and layering and criteria combination were performed in Geographic Information System using the designed decision algorithm. The results from zoning the protected areas indicated that zone 1 and zone 2 constituted 5% and 11.38% of the total area, respectively. In other words, the protection zones covered over 16% of the study area. The results also indicated that the criteria linear combination model and the decision algorithm could facilitate accurate zoning of the areas while maintaining their ecologic and human values.

Given the abundance of solar irradiation in Africa, different types of Solar Water Heating technologies can offer practical and reasonable solutions that are valuable for African people and their environment. However, these technologies' impacts were not quantified, and lack of studies could be in part attributed to the weak implementation of solar water heating systems on most countries in Africa. The aim of this paper is first, a model was developed and validated to investigate and analyze the energy performances of the two most domestic used technologies in the African continent: the flat plate collectors and the evacuated tube collectors. Next, the economic and environmental aspects of these systems are also presented. The study was carried out for 43 countries divided in 5 regions: North Africa, West Africa, Central Africa, East Africa and Southern Africa. The energy analysis, in terms of solar fraction, shows that the solar production of domestic hot water varies between 25 and 88% for the evacuated tube and between 10 and 60% for the flat plat collector and this according to the 5 regions mentioned above. Based on the Net present worth, the Benefit–Cost Ratio and the Discounted payback period, the economic analysis shows that these two technologies are beneficial for 16 of the 43 countries studied. That is due to the abundance of other energy resources in the remaining countries.

Caspian Sea as the largest inland water body plays an important role in economy of its neighbor countries apart from its environmental significance. This study explores wave climate in three locations in the southern Caspian Sea considering the socioeconomic pathway scenarios. In this regard, significant wave height, peak wave period and potential wave energy have been projected for a 20-year period of 2081–2100. To achieve reliable forecasts of wave characteristics and also to manipulate different sources of uncertainty, a new statistical framework is employed. The proposed framework gain Weibull distribution based technique for regionalization of near surface wind speed obtained from the climate model to feed the ANN (artificial neural network) model used for wave modeling. Furthermore, a post processing approach is used to ascertain consistency of the distribution of the model forecasts with those of the reference data. Considering two climate change scenarios, it was found that significant wave height and peak wave period do not change remarkably in terms of annual and seasonal variability when compared to the present climate. Moreover, wave energy analysis for the historical and future periods demonstrated sustainability of this renewable energy resources which is promising since clean energies are attracting much attention to meet the increasing demand for electricity. In the southern Caspian Sea, the middle part of the sea is subject to stronger waves indicating its higher potential for energy extraction than the western and eastern parts.

In recent years, the anthropogenic activities has increased the radiative budget of the earth which has been gaining significance. Anthropocene (human-influenced) era has altered the characteristic nature of the earth in a relatively shorter period with the accumulation of undesirable components in different spheres of earth. BC is one such component with severe implications in determining the fate of the environment. Hence, a comprehensive review has been conducted using meta-analyses of 675 studies spanning from 2009 to 2019 on a global scale to address the role of BC in a different environment, emphasizing sources, properties, transport, limitation, and uncertainties. Key findings of this review on the role of BC suggest that research directions have focused on the following topics as (1) fate of toxicants stored in the BC after deposition in the different environment; (2) role of BC on soil temperature with the perspective of agricultural production; and (3) atmospheric studies on large-scale coverage of mixing state chemistry and models. This review is designed to provide essential information on the current state of BC in a different environment and provide platforms for new conceptual frameworks. It also will help policymakers in scheming climate mitigation efforts.

Renewable energy research in China often underestimates the impact of non-technical or human factors on adoption, as well as the impacts of these factors on energy inequality. This review investigates how social-psychological and demographic factors influence the adoption of electric vehicles, photovoltaic panels, solar water heaters, and smart home technology, especially in rural China. Renewable energy technology adoption in China is extensive, but many social-psychological and demographics barriers to greater diffusion have not been addressed in research or policy. Studies suggest that subjective norms, perceived behavioral control, government support, and knowledge about the technology are the most important social-psychological factors affecting adoption intention in China. Demographic factors and concomitant equity issues have received little attention are largely unstudied in China-specific research and constitutes a major research gap. The limited available literature suggests that the significant demographic factors are, income, urban versus rural setting, education, and cultural values weighted towards collectivism and uncertainty avoidance. We conclude with future research opportunities and policy recommendations.

Population growth and industrial development have resulted in the physical development of cities and transformation of land use, especially in the areas surrounding the City. Reviewing these changes and predicting urban growth through modelling can help environmental managers in making more informed decisions. The purpose of this research was to simulate the expansion of urbanization and also its prediction in the future using the SLEUTH model in Zanjan. In this study, Landsat and IRS images were used 2001, 2002, and 2003. Then, slope data, land use, exclusion, urban development, transportation, and landing were introduced to the Cygwin using the controlled classification method, to implement the SLEUTH model. The results of model prediction indicated a significant increase will happen in urban areas by 38.1% over the next 22 years. The results also showed that with SLEUTH model calibration, the projected growth has a high scattering coefficient, in which case topography limits the development and expansion of the City. In contrast, the coefficients of spread and slope of play with points of 96 and 94 have considerably grown, respectively. The conversion of these lands into urban areas will slow down from now until the next 22 years, and this further development will be achieved along with the main roads of the province, as well as the actual results of the current situation will be the evidence for that. The results of this study can provide further study opportunities for planners and decision makers to consider preventive and controlling factors into strategic decisions.

Given the importance of mining as a source of Total Suspended Particles (TSP) and fine Particulate Matters (PM10), it is important to develop appropriate methods for estimating emissions for use in modeling forecasts and to be aware of regulatory decisions. Development of site-specific emission estimation and also dispersion radius in copper mines would be a practical guide for environmental regulatory bodies in developing countries with incomplete local data. To address these needs, this research is presented, at an open-cast copper mine in mountainous area, using both one-year measurement observational and AERMOD emission modeling software based on documented Environment Protection Agency emission factors. Results demonstrate TSP dispersion predictions were in better agreement with the measured concentrations than those for PM10. This research finds that PM10 and TSP concentration are lowest in autumn and winter, respectively, while both have the highest concentration in summer. The maximum observed concentration of both pollutants is about 300 μg/m3 which is beyond the standard level. The study further shows that generally, concentration of TSP is three times of PM10. The dispersion radius of aerosols was 14 km with wide dispersion for TSP compared with PM10; so, could potentially pose serious environmental and health threats to the surrounding environment and community. The TSP and PM10 emission are about 3.242 and 0.969 kg per any ton of copper produced. This research was applied to a mountainous copper mine in NW Iran, one of the biggest surface mines all across the world (~ 300Mt/y).

Concerns surrounding climate change are increasing pressure on policymakers to reduce greenhouse gas emissions. A number of policy mechanisms, including Pigouvian taxes on carbon and subsidies for and mandates on the use of carbon-free generation, are among the levers that are used to achieve this goal. A challenge that such mechanisms present is that they can distort energy markets, affecting prices and the financial viability of investing in or maintaining generating capacity. We survey literature pertaining to how policy mechanisms impact markets and short- and long-run economic signals. We present also a case study that examines these issues. Our case study uses a two-step modeling procedure. First, a long-term generation- and transmission-planning model determines the optimal technology mix that should be built to serve future loads under different climate-policy regimes. Next, a unit-commitment model is used to simulate hourly operation of the system. Combining the results of these two modeling steps, we assess the efficiency of using different policy mechanisms to achieve carbon reductions. Based on our literature survey and case study, we find that market-based policies (e.g., carbon taxes) achieve decarbonization targets most efficiently. Moreover, market-based policies are the least distortionary in terms of price formation in wholesale electricity markets.

Due to the frequent urban air pollution episodes over Kuwait recently, decision-makers and government agencies are struggling for sustainable strategies to optimize urban land use and land cover change (LUCC) and improve air quality. This article is targeting to identify the underlying relationships between dust concentration variations and LUCC, using the numerical modelling approach. The RegCM4 and WRF-CHEM models were employed to explore the impacts of land use change over Kuwait to be Evergreen Broad-leaf instead of Desert. Results reveal that both models performed good estimate in two severe dust storm cases, as they detected these cases with reasonable concentrations compared to the reanalysis data with positive correlation, and the overall mean dust concentrations in the target area declined by approximately 6–50% using RegCM4 and 25% with WRF in both dust episodes. Besides, the LUCC affected the wind directions around the area of LUCC; however, it had no impact on the wind’s strength. These results suggested that LUCC caused by an increase in long trees might be an important factor for the PM10 concentration reduction in Kuwait and would need to be investigated over a longer period.

Reducing carbon dioxide (CO2) anthropogenic emissions is an essential goal for combating climate change, and firms and policymakers must be aware of the main polluting areas of intervention for health, economic and environmental improvement, and mapping emissions is a relevant instrument to measure their geospatial distribution in order to reach that goal. Verified carbon emissions within the European Union mechanisms are a direct source of information that has not been previously used for mapping purposes. Through the application of universal kriging techniques, the proposed model shows the spatial variations of CO2 industrial emissions in the Spanish provinces and the contribution of the most polluting sectors, helping to understand the spatial CO2 emission dynamics in order to establish adequate environmental policies.

The continuous increase of air pollution is an emerging environmental issue in the Indian megacities for the last few decades. Rapid and uncontrolled urbanization coupled with population growth, rising vehicle population, and growth of industries has been fuelling this problem. The present study analyzed the trend and pattern of air pollution of three Indian megacities: Delhi, Kolkata, and Chennai in a spatio-temporal frame using a comparative approach. To develop the air pollution scenario, air-quality data have been collected from the Central Pollution Control Board and also the State Pollution Control Board of respective cities. Four major pollutants (PM10, PM2.5, NO2, and SO2) have been selected to develop the air-quality index (AQI) from the period of 2017 to 2020. The meteorological parameters have been used to correlate with AQI. Moreover, exceedance factor has been calculated to analyze the level of pollution in comparison to the national standards. The results demonstrate that Delhi and Kolkata are most affected by air pollution. The seasonal distribution shows that higher concentrations of pollution were found during the winter season. The results of this analysis will be helpful in the assessment of air pollution and to investigate the way out through proper policies.

This paper illustrates how Machine Learning techniques can be used to assess the impact of environmental protocols that are sparsely activated over time. A case study is analysed: the impact of a protocol that sets traffic restrictions on NO2 levels in Madrid’s urban area. The protocol specifies that these restrictions are active only when NO2 level reaches above a certain threshold. Since the protocol was first enacted, in 2017, restrictions have only been active for 59 days, never longer than ten consecutive days. Cross effects are identified: the protocol magnifies the effect of other relevant features, especially wind speed. Pollution decreases with wind speed. Consequently, episodes of high pollution levels generally occur when wind speed is low. The analysis shows that precisely at these low values of wind speed, an increase in the wind speed has a higher effect on pollution when the protocol is activated than when it is not. The analysis is carried out at a measuring station level, considering eight representative stations. The referred cross effect is clearer at centrally located stations across Madrid. Cross effects of the protocol with other weather features are weaker than that with wind speed. The assessment is based on the computation of Shapley values for classification trees that are built using XGBoost and rolling windows.