An application of the hybrid AHP-PROMETHEE approach to evaluate the severity of the factors influencing road accidents.

The evaluation of the severity of the factors influencing road accidents with a detailed severity distribution is critical to plan evidence-based road safety improvements and strategies. However, currently available studies use statistical and machine learning (ML) models to evaluate the severity of factors causing road accidents without a detailed severity distribution. Further, most of these available models require significant pre-data processing and have certain data-centric limitations. However, the multi criteria decision-making (MCDM) techniques have the potential to combine expert opinions for robust analysis without any pre-data processing calculations. Thus, this study uses a hybrid analytic hierarchy process (AHP) and the preference ranking organisation method for enrichment evaluation (PROMETHEE) approach to analyse the severity of factors and characteristics that influence road accidents within the Gujarat state, using injury types as criteria and minor road accident influencing factors as alternatives. These 82 minor factors have been further characterised into 18 characteristics and 4 major factors. Further, AHP integrated 40 expert inputs to determine criterion weights, while PROMETHEE ranked all minor variables. Then, after applying k-mean clustering, each ranked factor has been classified as very severe, moderately severe, or severe. The result clearly highlights that overspeeding, male gender, and clear weather conditions have been concluded to be the highly severe factors for Gujarat state. Thus, by providing a clear severity analysis and distribution of factors influencing road accidents, the proposed research may help government stakeholders, researchers, and politicians build severity-based road safety reforms and strategies with clarity.

A nature-based solution selection framework: Criteria and processes for addressing hydro-meteorological hazards at open-air laboratories across Europe.

Nature-based solutions (NbS) can be beneficial to help human communities build resilience to climate change by managing and mitigating related hydro-meteorological hazards (HMHs). Substantial research has been carried out in the past on the detection and assessment of HMHs and their derived risks. Yet, knowledge on the performance and functioning of NbS to address these hazards is severely lacking. The latter is exacerbated by the lack of practical and viable approaches that would help identify and select NbS for specific problems. The EU-funded OPERANDUM project established seven Open-Air Laboratories (OALs) across Europe to co-develop, test, and generate an evidence base from innovative NbS deployed to address HMHs such as flooding, droughts, landslides, erosion, and eutrophication. Herein, we detail the original approaches that each OAL followed in the process of identifying and selecting NbS for specific hazards with the aim of proposing a novel, generic framework for selecting NbS. We found that the process of selecting NBS was overall complex and context-specific in all the OALs, and it comprised 26 steps distributed across three stages: (i) Problem recognition, (ii) NbS identification, and (iii) NbS selection. We also identified over 20 selection criteria which, in most cases, were shared across OALs and were chiefly related to sustainability aspects. All the identified NbS were related to the regulation of the water cycle, and they were mostly chosen according to three main factors: (i) hazard type, (ii) hazard scale, and (iii) OAL size. We noticed that OALs exposed to landslides and erosion selected NbS capable to manage water budgets within the soil compartment at the local or landscape scale, while OALs exposed to floods, droughts, and eutrophication selected approaches to managing water transport and storage at the catchment scale. We successfully portrayed a synthesis of the stages and steps followed in the OALs' NbS selection process in a framework. The framework, which reflects the experiences of the stakeholders involved, is inclusive and integrated, and it can serve as a basis to inform NbS selection processes whilst facilitating the organisation of diverse stakeholders working towards finding solutions to natural hazards. We animate the future development of the proposed framework by integrating financial viability steps. We also encourage studies looking into the implementation of the proposed framework through quantitative approaches integrating multi-criteria analyses.

Resilient landscape pattern for reducing coastal flood susceptibility.

Evaluating flood susceptibility, identifying flood-prone areas, and planning reasonable landscape patterns are important measures in promoting sustainable urban development and flood mitigation. To this end, this study evaluated the flood susceptibility using a neural network model depending on a flood inundation map created from satellite data from 2010 to 2020, and explanatory factors for flood inundation selected by Geodetector and regularized random forest. Subsequently, the landscape pattern of the coastal city was quantified based on the land cover, and key landscape pattern metrics for flood susceptibility were selected at patch and class levels using statistical approaches. Eventually, urban spatial planning strategies for flood management were proposed based on the ecological significance of key metrics. Taking Xiamen as a case study, the flood susceptibility map showed that flood-prone areas in Xiamen are mainly distributed along river banks and coastlines. Key landscape pattern metrics for flood susceptibility selected by statistical approaches showed that patch-level metrics account for more explanatory power than class-level metrics, and the classes of the landscape would affect the role of patch-level metrics. Overall, the division index of the forest, the connectance index of water, the number of core areas and the fractal dimension index of urban, and the Euclidean nearest-neighbor distance of urban and water are significantly positively related to flood susceptibility, while the core area and the proximity index of urban, the similarity index, the core area index, and the edge contrast index of the forest, and the contiguity index of forest, grass, farmland, and shrub negatively related with flood susceptibility. Based on these findings, intensive urban planning and integrative Nature-based Solutions networks should be considered as strategies for enhancing coastal flood resilience.

Development of automated marine floating plastic detection system using Sentinel-2 imagery and machine learning models.

The increasing level of marine plastic pollution poses severe threats to the marine ecosystem and biodiversity. Open remote sensing data and advanced machine learning (ML) algorithms could be a cost-effective solution for identifying large plastic patches across the scale. The potential application of such resources in detecting and discriminating marine floating plastics (MFP) are not fully explored. Therefore, the present study attempted to explore the full functionality of open Sentinel satellite data and ML models for detecting and classifying the MFP in Mytilene (Greece), Limassol (Cyprus), Skala Loutron, Greece, Calabria (Italy), and Beirut (Lebanon). Two ML models, i.e. Support Vector Machine (SVM) and Random Forest (RF), were utilized to perform the classification analysis. In-situ plastic location data was collected from the control experiments conducted in Mytilene, Greece (in 2018 and 2019), Skala Loutron, Greece (2021), and Limassol, Cyprus (2018), and the same was considered for training the models. The accuracy and performances of the trained models were further tested on unseen new data collected from Calabria, Italy and Beirut, Lebanon. Both remote sensing bands and spectral indices were used for developing the ML models. A spectral signature profile for marine plastic was created for discriminating the floating plastic from other marine debris. A newly developed index, kernel Normalized Difference Vegetation Index (kNDVI), was incorporated into the modelling to examine its contribution to model performances. Both SVM and RF were performed well in five models and test case combinations. Among the two ML models, the highest performance was measured for the RF. The inclusion of kNDVI was found effective and increased the model performances, reflected by high balanced accuracy measured for model 2 (~89% to ~100% for SVM and ~92% to ~98% for RF). An automated floating plastic detection system was developed and tested in Calabria and Beirut using the best-performed model. The trained model had detected the floating plastic for both sites with ~80%-90%% accuracy. Among the six predictors, the Floating Debris Index (FDI) was the most important variable for detecting marine floating plastic. These findings collectively suggest that high-resolution remote sensing imagery and the automated ML models can be an effective alternative for the cost-effective detection of MFP. Future research will be directed toward collecting quality training data to develop robust automated models and prepare a spectral library for different plastic objects for discriminating plastic from other marine floating debris and advancing the marine plastic pollution research by taking full advantage of open-source data and technologies.

Examining the status of forest fire emission in 2020 and its connection to COVID-19 incidents in West Coast regions of the United States.

Forest fires impact on soil, water, and biota resources. The current forest fires in the West Coast of the United States (US) profoundly impacted the atmosphere and air quality across the ecosystems and have caused severe environmental and public health burdens. Forest fire led emissions could significantly exacerbate the air pollution level and, therefore, would play a critical role if the same occurs together with any epidemic and pandemic health crisis. Limited research is done so far to examine its impact in connection to the current pandemic. As of October 21, nearly 8.2 million acres of forest area were burned, with more than 25 casualties reported so far. In-situ air pollution data were utilized to examine the effects of the 2020 forest fire on atmosphere and coronavirus (COVID-19) casualties. The spatial-temporal concentrations of particulate matter (PM2.5 and PM10) and Nitrogen Dioxide (NO2) were collected from August 1 to October 30 for 2020 (the fire year) and 2019 (the reference year). Both spatial (Multiscale Geographically Weighted Regression) and non-spatial (Negative Binomial Regression) analyses were performed to assess the adverse effects of fire emission on human health. The in-situ data-led measurements showed that the maximum increases in PM2.5, PM10, and NO2 concentrations (µg/m3) were clustered in the West Coastal fire-prone states during August 1 - October 30, 2020. The average concentration (µg/m3) of particulate matter (PM2.5 and PM10) and NO2 was increased in all the fire states severely affected by forest fires. The average PM2.5 concentrations (µg/m3) over the period were recorded as 7.9, 6.3, 5.5, and 5.2 for California, Colorado, Oregon, and Washington in 2019, increasing up to 24.9, 13.4, 25.0, and 17.0 in 2020. Both spatial and non-spatial regression models exhibited a statistically significant association between fire emission and COVID-19 incidents. Such association has been demonstrated robust and stable by a total of 30 models developed for analyzing the spatial non-stationary and local association. More in-depth research is needed to better understand the complex relationship between forest fire emission and human health.

COLAEVA: Visual Analytics and Data Mining Web-Based Tool for Virtual Coaching of Older Adult Populations.

The global population is aging in an unprecedented manner and the challenges for improving the lives of older adults are currently both a strong priority in the political and healthcare arena. In this sense, preventive measures and telemedicine have the potential to play an important role in improving the number of healthy years older adults may experience and virtual coaching is a promising research area to support this process. This paper presents COLAEVA, an interactive web application for older adult population clustering and evolution analysis. Its objective is to support caregivers in the design, validation and refinement of coaching plans adapted to specific population groups. COLAEVA enables coaching caregivers to interactively group similar older adults based on preliminary assessment data, using AI features, and to evaluate the influence of coaching plans once the final assessment is carried out for a baseline comparison. To evaluate COLAEVA, a usability test was carried out with 9 test participants obtaining an average SUS score of 71.1. Moreover, COLAEVA is available online to use and explore.

Nature-based solutions efficiency evaluation against natural hazards: Modelling methods, advantages and limitations.

Nature-based solutions (NBS) for hydro-meteorological risks (HMRs) reduction and management are becoming increasingly popular, but challenges such as the lack of well-recognised standard methodologies to evaluate their performance and upscale their implementation remain. We systematically evaluate the current state-of-the art on the models and tools that are utilised for the optimum allocation, design and efficiency evaluation of NBS for five HMRs (flooding, droughts, heatwaves, landslides, and storm surges and coastal erosion). We found that methods to assess the complex issue of NBS efficiency and cost-benefits analysis are still in the development stage and they have only been implemented through the methodologies developed for other purposes such as fluid dynamics models in micro and catchment scale contexts. Of the reviewed numerical models and tools MIKE-SHE, SWMM (for floods), ParFlow-TREES, ACRU, SIMGRO (for droughts), WRF, ENVI-met (for heatwaves), FUNWAVE-TVD, BROOK90 (for landslides), TELEMAC and ADCIRC (for storm surges) are more flexible to evaluate the performance and effectiveness of specific NBS such as wetlands, ponds, trees, parks, grass, green roof/walls, tree roots, vegetations, coral reefs, mangroves, sea grasses, oyster reefs, sea salt marshes, sandy beaches and dunes. We conclude that the models and tools that are capable of assessing the multiple benefits, particularly the performance and cost-effectiveness of NBS for HMR reduction and management are not readily available. Thus, our synthesis of modelling methods can facilitate their selection that can maximise opportunities and refute the current political hesitation of NBS deployment compared with grey solutions for HMR management but also for the provision of a wide range of social and economic co-benefits. However, there is still a need for bespoke modelling tools that can holistically assess the various components of NBS from an HMR reduction and management perspective. Such tools can facilitate impact assessment modelling under different NBS scenarios to build a solid evidence base for upscaling and replicating the implementation of NBS.

Exploring spatiotemporal effects of the driving factors on COVID-19 incidences in the contiguous United States.

Since December 2019, the world has witnessed the stringent effect of an unprecedented global pandemic, coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of January 29,2021, there have been 100,819,363 confirmed cases and 2,176,159 deaths reported. Among the countries affected severely by COVID-19, the United States tops the list. Research has been conducted to discuss the causal associations between explanatory factors and COVID-19 transmission in the contiguous United States. However, most of these studies focus more on spatial associations of the estimated parameters, yet exploring the time-varying dimension in spatial econometric modeling appears to be utmost essential. This research adopts various relevant approaches to explore the potential effects of driving factors on COVID-19 counts in the contiguous United States. A total of three global spatial regression models and two local spatial regression models, the latter including geographically weighted regression (GWR) and multiscale GWR (MGWR), are performed at the county scale to take into account the scale effects. For COVID-19 cases, ethnicity, crime, and income factors are found to be the strongest covariates and explain most of the variance of the modeling estimation. For COVID-19 deaths, migration (domestic and international) and income factors play a critical role in explaining spatial differences of COVID-19 deaths across counties. Such associations also exhibit temporal variations from March to July, as supported by better performance of MGWR than GWR. Both global and local associations among the parameters vary highly over space and change across time. Therefore, time dimension should be paid more attention to in the spatial epidemiological analysis. Among the two local spatial regression models, MGWR performs more accurately, as it has slightly higher Adj. R2 values (for cases, R2 = 0.961; for deaths, R2 = 0.962), compared to GWR's Adj. R2 values (for cases, R2 = 0.954; for deaths, R2 = 0.954). To inform policy-makers at the nation and state levels, understanding the place-based characteristics of the explanatory forces and related spatial patterns of the driving factors is of paramount importance. Since it is not the first time humans are facing public health emergency, the findings of the present research on COVID-19 therefore can be used as a reference for policy designing and effective decision making.

Examining the status of improved air quality in world cities due to COVID-19 led temporary reduction in anthropogenic emissions.

Clean air is a fundamental necessity for human health and well-being. Anthropogenic emissions that are harmful to human health have been reduced substantially under COVID-19 lockdown. Satellite remote sensing for air pollution assessments can be highly effective in public health research because of the possibility of estimating air pollution levels over large scales. In this study, we utilized both satellite and surface measurements to estimate air pollution levels in 20 cities across the world. Google Earth Engine (GEE) and Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI) application were used for both spatial and time-series assessment of tropospheric Nitrogen Dioxide (NO2) and Carbon Monoxide (CO) statuses during the study period (1 February to May 11, 2019 and the corresponding period in 2020). We also measured Population-Weighted Average Concentration (PWAC) of particulate matter (PM2.5 and PM10) and NO2 using gridded population data and in-situ air pollution estimates. We estimated the economic benefit of reduced anthropogenic emissions using two valuation approaches: (1) the median externality value coefficient approach, applied for satellite data, and (2) the public health burden approach, applied for in-situ data. Satellite data have shown that ~28 tons (sum of 20 cities) of NO2 and ~184 tons (sum of 20 cities) of CO have been reduced during the study period. PM2.5, PM10, and NO2 are reduced by ~37 (µg/m3), 62 (µg/m3), and 145 (µg/m3), respectively. A total of ~1310, ~401, and ~430 premature cause-specific deaths were estimated to be avoided with the reduction of NO2, PM2.5, and PM10. The total economic benefits (Billion US$) (sum of 20 cities) of the avoided mortality are measured as ~10, ~3.1, and ~3.3 for NO2, PM2.5, and PM10, respectively. In many cases, ground monitored data was found inadequate for detailed spatial assessment. This problem can be better addressed by incorporating satellite data into the evaluation if proper quality assurance is achieved, and the data processing burden can be alleviated or even removed. Both satellite and ground-based estimates suggest the positive effect of the limited human interference on the natural environments. Further research in this direction is needed to explore this synergistic association more explicitly.

Evaluating the plausible application of advanced machine learnings in exploring determinant factors of present pandemic: A case for continent specific COVID-19 analysis.

Coronavirus disease, a novel severe acute respiratory syndrome (SARS COVID-19), has become a global health concern due to its unpredictable nature and lack of adequate medicines. Machine Learning (ML) models could be effective in identifying the most critical factors which are responsible for the overall fatalities caused by COVID-19. The functional capabilities of ML models in epidemiological research, especially for COVID-19, are not substantially explored. To bridge this gap, this study has adopted two advanced ML models, viz. Random Forest (RF) and Gradient Boosted Machine (GBM), to perform the regression modelling and provide subsequent interpretation. Five successive steps were followed to carry out the analysis: (1) identification of relevant key explanatory variables; (2) application of data dimensionality reduction for eliminating redundant information; (3) utilizing ML models for measuring relative influence (RI) of the explanatory variables; (4) evaluating interconnections between and among the key explanatory variables and COVID-19 case and death counts; (5) time series analysis for examining the rate of incidences of COVID-19 cases and deaths. Among the explanatory variables considered in this study, air pollution, migration, economy, and demographic factor were found to be the most significant controlling factors. Since a very limited research is available to discuss the superiority of ML models for identifying the key determinants of COVID-19, this study could be a reference for future public health research. Additionally, all the models and data used in this study are open source and freely available, thereby, reproducibility and scientific replication will be achievable easily.

Spatial variability of urban forest topsoil properties: towards representative and robust sampling design.

Background: Soil spatial variability is a major concern when deciding how to collect a representative topsoil sample for laboratory analysis. Sampling design to capture site-specific variability is documented in the agricultural literature, but poorly understood for urban forest soils where soils may be characterized by strong horizontal and vertical variability and large temporal anthropogenic disturbances. Methods: This paper evaluates the spatial variability of selected topsoil properties under urban trees to define a statistically robust sampling design that optimizes the number of samples to reliably characterize basal soil respiration (BSR), a property associated with soil health. To provide a reference on variability, two additional soil properties were measured, unrelated to BSR: electrical conductivity (EC) and bulk density (BD). Thirteen sampling sites comprising both park and street trees ( Acer rubrum) were selected in Cambridge, MA, USA. Results: Results indicate street tree topsoil had approximately twice as much variation, requiring more intensive sampling, as did park tree topsoil, even though street trees had smaller soil sampling zones, constricted by tree pits. The variability of BSR was nearly identical to that of EC, and BD results varied least. A large number of samples would be required for acceptable levels of statistical reliability (90% CI - 10% ER) of 44.4, 41.7, and 6.4 for BSR, EC, and BD, respectively, whereas by accepting a lower level of certainty (80% CI - 20% ER) the number of required soil samples was calculated as 6.8, 6.4, and 0.4 for BSR, EC, and BD, respectively. Conclusions: The use of EC testing as a baseline measure to determine spatial variation in the topsoil is proposed, to alleviate the financial implications of more expensive BSR testing. Factors of topsoil disturbance and soil access restrictions at sites with severe root-sidewalk conflicts and the overall generalizability of the results are also discussed.

Leveraging artificial intelligence to analyze the COVID-19 distribution pattern based on socio-economic determinants.

The spatialization of socioeconomic data can be used and integrated with other sources of information to reveal valuable insights. Such data can be utilized to infer different variations, such as the dynamics of city dwellers and their spatial and temporal variability. This work focuses on such applications to explore the underlying association between socioeconomic characteristics of different geographical regions in Dublin, Ireland, and the number of confirmed COVID cases in each area. Our aim is to implement a machine learning approach to identify demographic characteristics and spatial patterns. Spatial analysis was used to describe the pattern of interest in electoral divisions (ED), which are the legally defined administrative areas in the Republic of Ireland for which population statistics are published from the census data. We used the most informative variables of the census data to model the number of infected people in different regions at ED level. Seven clusters detected by implementing an unsupervised neural network method. The distribution of people who have contracted the virus was studied.

Examining the effects of green revolution led agricultural expansion on net ecosystem service values in India using multiple valuation approaches.

Ecosystem Services (ESs) are bundles of natural processes and functions that are essential for human well-being, subsistence, and livelihoods. The 'Green Revolution' (GR) has substantial impact on the agricultural landscape and ESs in India. However, the effects of GR on ESs have not been adequately documented and analyzed. This leads to the main hypothesis of this work - 'the incremental trend of ESs in India is mainly prompted by GR led agricultural innovations that took place during 1960 - 1970'. The analysis was carried out through five successive steps. First, the spatiotemporal Ecosystem Service Values (ESVs) in Billion US$ for 1985, 1995, and 2005 were estimated using several value transfer approaches. Second, the sensitivity and elasticity of different ESs to land conversion were carried out using coefficient of sensitivity and coefficient of elasticity. Third, the Geographically Weighted Regression model was performed using five explanatory factors, i.e., total crop area, crop production, crop yield, net irrigated area, and cropping intensity, to explore the cumulative and individual effects of these driving factors on ESVs. Fourth, Multi-Layer Perceptron based Artificial Neural Network was employed to estimate the normalized importance of these explanatory factors. Fifth, simple and multiple linear regression modeling was done to assess the linear associations between the driving factors and the ESs. During the observation periods, cropland, forestland and water bodies contributed to 80%-90% of ESVs, followed by grassland, mangrove, wetland and urban built-up. In all three evaluation years, the highest estimated ESVs among the nine ES categories was provided by water regulation, followed by soil formation and soil-water retention, biodiversity maintenance, waste treatment, climate regulation, and greenhouse gas regulation. Among the five explanatory factors, total crop area, crop production, and net irrigated area showed strong positive associations with ESVs, while cropping intensity exhibited a negative association. Therefore, the study reveals a strong association between GR led agricultural expansion and ESVs in India. This study suggests that there should be an urgent need for formulation of rigorous ecosystem management strategies and policies to preserve ecological integrity and flow of uninterrupted ESs and to sustain human well-being.

Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach.

The socio-demographic factors have a substantial impact on the overall casualties caused by the Coronavirus (COVID-19). In this study, the global and local spatial association between the key socio-demographic variables and COVID-19 cases and deaths in the European regions were analyzed using the spatial regression models. A total of 31 European countries were selected for modelling and subsequent analysis. From the initial 28 socio-demographic variables, a total of 2 (for COVID-19 cases) and 3 (for COVID-19 deaths) key variables were filtered out for the regression modelling. The spatially explicit regression modelling and mapping were done using four spatial regression models such as Geographically Weighted Regression (GWR), Spatial Error Model (SEM), Spatial Lag Model (SLM), and Ordinary Least Square (OLS). Additionally, Partial Least Square (PLS) and Principal Component Regression (PCR) was performed to estimate the overall explanatory power of the regression models. For the COVID cases, the local R2 values, which suggesting the influences of the selected socio-demographic variables on COVID cases and death, were found highest in Germany, Austria, Slovenia, Switzerland, Italy. The moderate local R2 was observed for Luxembourg, Poland, Denmark, Croatia, Belgium, Slovakia. The lowest local R2 value for COVID-19 cases was accounted for Ireland, Portugal, United Kingdom, Spain, Cyprus, Romania. Among the 2 variables, the highest local R2 was calculated for income (R2 = 0.71), followed by poverty (R2 = 0.45). For the COVID deaths, the highest association was found in Italy, Croatia, Slovenia, Austria. The moderate association was documented for Hungary, Greece, Switzerland, Slovakia, and the lower association was found in the United Kingdom, Ireland, Netherlands, Cyprus. This suggests that the selected demographic and socio-economic components, including total population, poverty, income, are the key factors in regulating overall casualties of COVID-19 in the European region. In this study, the influence of the other controlling factors, such as environmental conditions, socio-ecological status, climatic extremity, etc. have not been considered. This could be the scope for future research.

A primary school driven initiative to influence commuting style for dropping-off and picking-up of pupils.

The use of cars for drop-off and pick-up of pupils from schools is a potential cause of pollution hotspots at school premises. Employing a joint execution of smart sensing technology and citizen science approach, a primary school took an initiative to co-design a study with local community and researchers to generate data and provide information to understand the impact on pollution levels and identify possible mitigation measures. This study was aimed to assess the hotspots of vehicle-generated particulate matter ≤2.5 µm (PM2.5) and ≤10 µm (PM10) at defined drop-off/pick-up points and its ingress into a nearby naturally ventilated primary school classroom. Five different locations were selected inside school premises for measurements during two peak hours: morning (MP; 0730-0930 h; local time), evening (EP; 1400-1600 h), and off-peak (OP; 1100-1300 h) hours for comparison. These represent PM measurements at the main road, pick-up point at the adjoining road, drop-off point, a classroom, and the school playground. Additional measurements of carbon dioxide (CO2) were taken simultaneously inside and outside (drop-off point) the classroom to understand its build-up and ingress of outdoor PM. The results demonstrated nearly a three-fold increase in the concentrations of fine particles (PM2.5) during drop-off hours compared to off-peak hours indicated the dominant contribution of car queuing in the school premises. Coarse particles (PM2.5-10) were prevalent in the school playground, while the contribution of fine particles as a result of traffic congestion became more pronounced during drop-off hours. In the naturally ventilated classroom, the changes in indoor PM2.5 concentrations during both peak hours (0.58 < R2 < 0.67) were followed by the outdoor concentration at the drop-off point. This initiative resulted in valuable information that might be used to influence school commuting style and raise other important issues such as the generally fairly high PM2.5 concentrations in the playground and future classroom ventilation plans.

Identification of Conservation Priority Zones Using Spatially Explicit Valued Ecosystem Services: A Case from the Indian Sundarbans.

Demarcation of conservation priority zones (CPZs) using spatially explicit models is the new challenge in ecosystem services (ESs) research. This study identifies the CPZs of the Indian Sundarbans by integrating 2 different approaches, that is, ESs and ecosystem health (EH). Five successive steps were followed to conduct the analysis: First, the ESs were estimated using biophysical and economic methods and a hybrid method (that combines biophysical and economic methods); second, the vigor-organization-resilience (VOR) model was used for estimating EH; third, the risk characterization value (RCV) of ESs was measured using the function of EH and ESs; fourth, Pearson correlation test was performed to analyze the interaction between ESs and EH components; and fifth, the CPZs were defined by considering 7 relevant components: ecosystem vigor, ecosystem organization, ecosystem resilience, RCV, EH, ESs, and the correlation between EH and ESs. Among the major ecoregions of the Sundarbans, the highest ESs value in economic terms is provided by the mangrove ecosystem (US$19 144.9 million per year). The highest conservation priority score was projected for the Gosaba block, which is dominated by dense mangrove forests. The estimated CPZs were found to be highly consistent with the existing biodiversity zonations. The outcome of this study could be a reference for environmentalists, land administrators, researchers, and decision makers to design relevant policies to protect the high values of the Sundarbans ecosystem. Integr Environ Assess Manag 2020;16:773-787. © 2020 SETAC.

Towards an operationalisation of nature-based solutions for natural hazards.

Nature-based solutions (NBS) are being promoted as adaptive measures against predicted increasing hydrometeorological hazards (HMHs), such as heatwaves and floods which have already caused significant loss of life and economic damage across the globe. However, the underpinning factors such as policy framework, end-users' interests and participation for NBS design and operationalisation are yet to be established. We discuss the operationalisation and implementation processes of NBS by means of a novel concept of Open-Air Laboratories (OAL) for its wider acceptance. The design and implementation of environmentally, economically, technically and socio-culturally sustainable NBS require inter- and transdisciplinary approaches which could be achieved by fostering co-creation processes by engaging stakeholders across various sectors and levels, inspiring more effective use of skills, diverse knowledge, manpower and resources, and connecting and harmonising the adaptation aims. The OAL serves as a benchmark for NBS upscaling, replication and exploitation in policy-making process through monitoring by field measurement, evaluation by key performance indicators and building solid evidence on their short- and long-term multiple benefits in different climatic, environmental and socio-economic conditions, thereby alleviating the challenges of political resistance, financial barriers and lack of knowledge. We conclude that holistic management of HMHs by effective use of NBS can be achieved with standard compliant data for replicating and monitoring NBS in OALs, knowledge about policy silos and interaction between research communities and end-users. Further research is needed for multi-risk analysis of HMHs and inclusion of NBS into policy frameworks, adaptable at local, regional and national scales leading to modification in the prevalent guidelines related to HMHs. The findings of this work can be used for developing synergies between current policy frameworks, scientific research and practical implementation of NBS in Europe and beyond for its wider acceptance.

Examining the effects of forest fire on terrestrial carbon emission and ecosystem production in India using remote sensing approaches.

Remote sensing techniques are effectively used for measuring the overall loss of terrestrial ecosystem productivity and biodiversity due to forest fires. The current research focuses on assessing the impacts of forest fires on terrestrial ecosystem productivity in India during 2003-2017. Spatiotemporal changes of satellite remote sensing derived burn indices were estimated for both fire and normal years to analyze the association between forest fires and ecosystem productivity. Two Light Use Efficiency (LUE) models were used to quantify the terrestrial Net Primary Productivity (NPP) of the forest ecosystem using the open-source and freely available remotely sensed data. A novel approach (delta NPP/delta burn indices) is developed to quantify the effects of forest fires on terrestrial carbon emission and ecosystem production. During 2003-2017, the forest fire intensity was found to be very high (>2000) across the eastern Himalayan hilly region, which is mostly covered by dense forest and thereby highly susceptible to wildfires. Scattered patches of intense forest fires were also detected in the lower Himalayan and central Indian states. The spatial correlation between the burn indices and NPP were mainly negative (-0.01 to -0.89) for the fire-prone states as compared to the other neighbouring regions. Additionally, the linear approximation between the burn indices and NPP showed a positive relation (0.01 to 0.63), suggesting a moderate to high impact of the forest fires on the ecosystem production and terrestrial carbon emission. The present approach has the potential to quantify the loss of ecosystem productivity due to forest fires.

Responses of ecosystem services to natural and anthropogenic forcings: A spatial regression based assessment in the world's largest mangrove ecosystem.

Most of the Earth's Ecosystem Services (ESs) have experienced a decreasing trend in the last few decades, primarily due to increasing human dominance in the natural environment. Identification and categorization of factors that affect the provision of ESs from global to local scales are challenging. This study makes an effort to identify the key driving factors and examine their effects on different ESs in the Sundarbans region, India. We carry out the analysis following five successive steps: (1) quantifying biophysical and economic values of ESs using three valuation approaches; (2) identifying six major driving forces on ESs; (3) categorizing principal data components with dimensionality reduction; (4) constructing multivariate regression models with variance partitioning; (5) implementing six spatial regression models to examine the causal effects of natural and anthropogenic forcings on ESs. Results show that climatic factors, biophysical factors, and environmental stressors significantly affect the ESs. Among the six driving factors, climate factors are highly associated with the ESs variation and explain the maximum model variances (R2 = 0.75-0.81). Socioeconomic (R2 = 0.44-0.66) and development (R2 = 27-0.44) factors have weak to moderate effects on the ESs. Furthermore, the joint effects of the driving factors are much higher than their individual effects. Among the six spatial regression models, Geographical Weighted Regression (GWR) performs the most accurately and explains the maximum model variances. The proposed hybrid valuation method aggregates biophysical and economic estimates of ESs and addresses methodological biases existing in the valuation process. The presented framework can be generalized and applied to other ecosystems at different scales. The outcome of this study could be a reference for decision-makers, planners, land administrators in formulating a suitable action plan and adopting relevant management practices to improve the overall socio-ecological status of the region.

Considerations for evaluating green infrastructure impacts in microscale and macroscale air pollution dispersion models.

Green infrastructure (GI) in urban areas may be adopted as a passive control system to reduce air pollutant concentrations. However, current dispersion models offer limited modelling options to evaluate its impact on ambient pollutant concentrations. The scope of this review revolves around the following question: how can GI be considered in readily available dispersion models to allow evaluation of its impacts on pollutant concentrations and health risk assessment? We examined the published literature on the parameterisation of deposition velocities and datasets for both particulate matter and gaseous pollutants that are required for deposition schemes. We evaluated the limitations of different air pollution dispersion models at two spatial scales - microscale (i.e. 10-500 m) and macroscale (i.e. 5-100 km) - in considering the effects of GI on air pollutant concentrations and exposure alteration. We conclude that the deposition schemes that represent GI impacts in detail are complex, resource-intensive, and involve an abundant volume of input data. An appropriate handling of GI characteristics (such as aerodynamic effect, deposition of air pollutants and surface roughness) in dispersion models is necessary for understanding the mechanism of air pollutant concentrations simulation in presence of GI at different spatial scales. The impacts of GI on air pollutant concentrations and health risk assessment (e.g., mortality, morbidity) are partly explored. The i-Tree tool with the BenMap model has been used to estimate the health outcomes of annually-averaged air pollutant removed by deposition over GI canopies at the macroscale. However, studies relating air pollution health risk assessments due to GI-related changes in short-term exposure, via pollutant concentrations redistribution at the microscale and enhanced atmospheric pollutant dilution by increased surface roughness at the macroscale, along with deposition, are rare. Suitable treatments of all physical and chemical processes in coupled dispersion-deposition models and assessments against real-world scenarios are vital for health risk assessments.