Boletim mensal VigiAR 01/2024: Poluentes atmosféricos – Fontes poluidoras

O Programa em Saúde Ambiental relacionado a populações expostas à poluição do ar do Município de São Paulo (VIGIAR) tem por objetivo desenvolver ações de vigilância em saúde ambiental, para populações expostas aos poluentes atmosféricos, de forma a orientar medidas de prevenção, promoção da saúde e de atenção integral, conforme preconizado pelo Sistema Único de Saúde (SUS).

How does China's green credit policy affect the innovation of high-polluting enterprises? From the perspective of innovation quantity and quality.

Employing the "Green Credit Guidelines" implemented in 2012 as the basis for a quasi-natural experiment, this study applies the method of Difference-in-Differences(DID) to investigate the influence of the Green Credit Policy on both the quantity and quality of enterprise innovation. The outcomes of our analysis reveal that the policy has significantly boosted both the quantity and quality of innovation among enterprises identified as heavy polluters. It is noteworthy that the policy's positive impact on innovation quantity surpasses its positive effect on innovation quality. This substantiates that the Green Credit Policy effectively generates incentivizing outcomes for innovation among the heavy polluters, thereby verifying Porter's hypothesis within the domain of green credit in China. Furthermore, we find that the positive impact is more significant for enterprises with lower innovation capabilities, large-scale enterprises, state-owned enterprises, and those situated in both the Eastern and Western regions. Through these findings, this study illuminates a novel perspective on the interplay between the Green Credit Policy and enterprise innovation dynamics in China.

Environmental regulation, industrial structure and energy efficiency: Evidence from 30 provinces in China.

The government's environmental protection policy can significantly contribute to alleviating resource shortages and curbing environmental pollution, but the impact of various policy instruments implemented by the government on energy efficiency is unclear. Based on the panel data of 30 provinces in China from 2005 to 2021, this paper analyses the impact of environmental regulation and the industrial structure on energy efficiency from the perspective of resource taxes. The U-shaped relationship between environmental regulation and energy efficiency and between the optimization of industrial structure can significantly improve energy efficiency, and the optimization of industrial structure is conducive to weakening the initial inhibitory effect of environmental regulation. In addition, the analysis of regional heterogeneity showed that the impact of environmental regulation was stronger in the central and western regions, while the impact of industrial structure was stronger in the eastern and western regions. The conclusions of this study can help to expand the understanding of the relationship between environmental regulation and industrial structure on energy efficiency, provide policy enlightenment for the realization of green development and high-quality development, and provide Chinese examples and experiences for developing countries to improve energy efficiency.

Multispecies and multibiomarker assessment of fish health from Iguaçu River reservoir, Southern Brazil.

This study investigated the impact of micropollutants on fish health from Segredo hydroelectric reservoir (HRS) along the Iguaçu River, Southern Brazil, contaminated by urban, industrial, and agricultural activities. This is the first comprehensive study assessment in the river after the severe drought in the 2020s in three fish species from different trophic levels Astyanax spp. (water column depth/omnivorous), Hypostomus commersoni (demersal/herbivorous), and Pimelodus maculatus (demersal/omnivorous). Animals, water, and sediment samples were collected from three distinct sites within the reservoir: Floresta (upstream), Iratim (middle), and Station (downstream). The chemical analysis revealed elevated concentrations of metals (Al, Cu, Fe) and the metalloid As in water, or Cu, Zn, and As in sediment, surpassing Brazilian regulatory limits, while the organic pollutants as DDT, PAHs, PCBs, and PBDEs were found under the Brazilian regulatory limits. The metal bioaccumulation was higher in gills with no significant differences among sites. The species Astyanax spp. and H. commersoni displayed variations in hepatosomatic index (HSI) and P. maculatus in the condition factor index (K) between sites, while adverse effects due to micropollutants bioaccumulation were observed by biochemical, genotoxic, and histopathological biomarkers. The principal component analysis and integrated biomarker response highlighted the upstream site Floresta as particularly inhospitable for biota, with distinctions based on trophic level. Consequently, this multifaceted approach, encompassing both fish biomarkers and chemical analyses, furnishes valuable insights into the potential toxic repercussions of micropollutant exposure. These findings offer crucial data for guiding management and conservation endeavors in the Iguaçu River.

Microbial community structure and metabolic profile of anthropized freshwater tributary channels from La Plata River, Argentina, to develop sustainable remediation strategies.

Microbial communities from freshwater sediments are involved in biogeochemical cycles and they can be modified by physical and chemical changes in the environment. Linking the microbial community structure (MCS) with physicochemistry of freshwater courses allows a better understanding of its ecology and can be useful to assess the ecological impact generated by human activity. The MCS of tributary channels from La Plata River affected by oil refinery (C, D, and E) and one also by urban discharges (C) was studied. For this purpose, 16S rRNA metabarcoding analysis, in silico metagenome functional prediction, and the hydrocarbon degradation potential (in silico predictions of hydrocarbon-degrading genes and their quantification by qPCR) of the MCS were studied. Principal coordinate analysis revealed that the MCS was different between sites, and it was not structured by the hydrocarbon content. Site C showed physicochemical characteristics, bacterial taxa, and an in silico functional prediction related to fermentative/heterotrophic metabolism. Site D, despite having higher concentration of hydrocarbon, presented autotrophic, syntrophic, and methanogenic pathways commonly involved in natural processes in anoxic sediments. Site E showed and intermediate autotrophic/heterotrophic behavior. The hydrocarbon degradation potential showed no positive correlation between the hydrocarbon-degrading genes quantified and predicted. The results suggest that the hydrocarbon concentration in the sites was not enough selection pressure to structure the bacterial community composition. Understanding which is the variable that structures the bacterial community composition is essential for monitoring and designing of sustainable management strategies for contaminated freshwater ecosystems.

Generation and classification of patch-based land use and land cover dataset in diverse Indian landscapes: a comparative study of machine learning and deep learning models.

In the context of environmental and social applications, the analysis of land use and land cover (LULC) holds immense significance. The growing accessibility of remote sensing (RS) data has led to the development of LULC benchmark datasets, especially pivotal for intricate image classification tasks. This study addresses the scarcity of such benchmark datasets across diverse settings, with a particular focus on the distinctive landscape of India. The study entails the creation of patch-based datasets, consisting of 4000 labelled images spanning four distinct LULC classes derived from Sentinel-2 satellite imagery. For the subsequent classification task, three traditional machine learning (ML) models and three convolutional neural networks (CNNs) were employed. Despite facing several challenges throughout the process of dataset generation and subsequent classification, the CNN models consistently attained an overall accuracy of 90% or more. Notably, one of the ML models stood out with 96% accuracy, surpassing CNNs in this specific context. The study also conducts a comparative analysis of ML models on existing benchmark datasets, revealing higher prediction accuracy when dealing with fewer LULC classes. Thus, the selection of an appropriate model hinges on the given task, available resources, and the necessary trade-offs between performance and efficiency, particularly crucial in resource-constrained settings. The standardized benchmark dataset contributes valuable insights into the relative performance of deep CNN and ML models in LULC classification, providing a comprehensive understanding of their strengths and weaknesses.

Comparative assessment of soil quality dynamics using SQI modelling approach: a study in rice bowl of West Bengal, India.

The study attempted to evaluate the agricultural soil quality using the Soil Quality Index (SQI) model in two Community Development Blocks, Ausgram-II and Memari-II of Purba Bardhaman District. Total 104 soil samples were collected (0-20 cm depth) from each Block to analyse 13 parameters (bulk density, soil porosity, soil aggregate stability, water holding capacity, infiltration rate, available nitrogen, available phosphorous, available potassium, soil pH, soil organic carbon, electrical conductivity, soil respiration and microbial biomass carbon) in this study. The Integrated Quality Index (IQI) was applied using the weighted additive approach and non-linear scoring technique to retain the Minimum Data Set (MDS). Principal Component Analysis (PCA) identified that SAS, BD, available K, pH, available N, and available P were the key contributing parameters to SQI in Ausgram-II. In contrast, WHC, SR, available N, pH, and SAS contributed the most to SQI in Memari-II. Results revealed that Ausgram-II (0.97) is notably higher SQI than Memari-II (0.69). In Ausgram-II, 99.72% of agricultural lands showed very high SQI (Grade I), whereas, in Memari-II, 49.95% of lands exhibited a moderate SQI (Grade III) and 49.90% showed a high SQI (Grade II). Sustainable Yield Index (SYI), Sensitivity Index (SI) and Efficiency Ratio (ER) were used to validate the SQIs. A positive correlation was observed between SQI and paddy ( R2 = 0.82 & 0.72) and potato yield (R2 = 0.71 & 0.78) in Ausgram-II and Memari-II Block, respectively. This study could evaluate the agricultural soil quality and provide insights for decision-making in fertiliser management practices to promote agricultural sustainability.

A Matter of Climate Justice: Heat and Air Pollution Combine to Worsen Effects of Homelessness.

Few studies on these concurrent health risks account for individuals without housing, yet they often experience greater exposure than other people-along with exacerbation of existing health issues.

Multi-vehicle adaptive 3D mapping for targeted ocean sampling.

Expanding spatial presentation from two-dimensional profile transects to three-dimensional ocean mapping is key for a better understanding of ocean processes. Phytoplankton distributions can be highly patchy and the accurate identification of these patches with the context, variability, and uncertainty of measurements on relevant scales is difficult to achieve. Traditional sampling methods, such as plankton nets, water samplers and in-situ vertical sensors, provide a snapshot and often miss the fine-scale horizontal and temporal variability. Here, we show how two autonomous underwater vehicles measured, adapted to, and reported real-time chlorophyll a measurements, giving insights into the spatiotemporal distribution of phytoplankton biomass and patchiness. To gain the maximum available information within their sensing scope, the vehicles moved in an adaptive fashion, looking for the regions of the highest predicted chlorophyll a concentration, the greatest uncertainty, and the least possibility of collision with other underwater vehicles and ships. The vehicles collaborated by exchanging data with each other and operators via satellite, using a common segmentation of the area to maximize information exchange over the limited bandwidth of the satellite. Importantly, the use of multiple autonomous underwater vehicles reporting real-time data combined with targeted sampling can provide better match with sampling towards understanding of plankton patchiness and ocean processes.

Evaluation of urban flood susceptibility through integrated Bivariate statistics and Geospatial technology.

Flood disasters are frequent natural disasters that occur annually during the monsoon season and significantly impact urban areas. This area is characterized by impermeable concrete surfaces, which increase runoff and are particularly susceptible to flooding. Therefore, this study aims to adopt Bi-variate statistical methods such as frequency ratio (FR) and weight of evidence (WOE) to map flood susceptibility in an urbanized watershed. The study area encompasses an urbanized watershed surrounding the Chennai Metropolitan area in southern India. The essential parameters considered for flood susceptibility zonation include geomorphology, soil, land use/land cover (LU/LC), rainfall, drainage, slope, aspect, Topographic Wetness Index (TWI), and Normalized Difference Vegetation Index (NDVI). The flood susceptibility map was derived using 70% of randomly selected flood areas from the flood inventory database, and the other 30% was used for validation using the area under curve (AUC) method. The AUC method produced a frequency ratio of 0.806 and a weight of evidence value of 0.865 contributing to the zonation of the three classes. The study further investigates the impact of urbanization on flood susceptibility and is further classified into high, moderate, and low flood risk zones. With the abrupt change in climatic scenarios, there is an increase in the risk of flash floods. The results of this study can be used by policymakers and planners in developing a preparedness system to mitigate economic, human, and property losses due to floods in any urbanized watershed.

Assessment of land use and land cover change detection and prediction using deep learning techniques for the southwestern coastal region, Goa, India.

Understanding the connections between human activities and the natural environment depends heavily on information about land use and land cover (LULC) in the form of accurate LULC maps. Environmental monitoring using deep learning (DL) is rapidly growing to preserve a sustainable environment in the long term. For establishing effective policies, regulations, and implementation, DL can be a valuable tool for assessing environmental conditions and natural resources that will positively impact the ecosystem. This paper presents the assessment of land use and land cover change detection (LULCCD) and prediction using DL techniques for the southwestern coastal region, Goa, also known as the tourist destination of India. It consists of three components: (i) change detection (CD), (ii) quantification of LULC changes, and (iii) prediction. A new CD assessment framework, Spatio-Temporal Encoder-Decoder Self Attention Network (STEDSAN), is proposed for the LULCCD process. A dual branch encoder-decoder network is constructed using strided convolution with downsampling for the encoder and transpose convolution with upsampling for the decoder to assess the bitemporal images spatially. The self-attention (SA) mechanism captures the complex global spatial-temporal (ST) interactions between individual pixels over space-time to produce more distinct features. Each branch accepts the LULC map of 2 years as one of its inputs to determine binary and multiclass changes among the bitemporal images. The STEDSAN model determines the patterns, trends, and conversion from one LULC type to another for the assessment period from 2005 to 2018. The binary change maps were also compared with the existing state of the art (SOTA) CD methods, with STEDSAN having an overall accuracy of 94.93%. The prediction was made using an recurrent neural network (RNN) known as long short term memory network (LSTM) for the year 2025. Experiments were conducted to determine area-wise changes in several LULC classes, such as built-up (BU), crops (kharif crop (KC), rabi crop (RC), zaid crop (ZC), double/triple (D/T C)), current fallow (CF), plantation (PL), forests (evergreen forest (EF), deciduous forest (DF), degraded/scurb forest (D/SF) ), littoral swamp (LS), grassland (GL), wasteland (WL), waterbodies max (Wmx), and waterbodies min (Wmn). As per the analysis, over the period of 13 years, there has been a net increase in the amount of BU (1.25%), RC (1.17%), and D/TC( 2.42%) and a net decrease in DF (3.29%) and WL(1.44%) being the most dominant classes being changed. These findings will offer a thorough description of identifying trends in coastal areas that may incorporate methodological hints for future studies. This study will also promote handling the spatial and temporal complexity of remotely sensed data employed in categorizing the coastal LULC of a heterogeneous landscape.

Lead Isotope Signatures and Source Identification of Heavy Metals in Vegetable Soils Irrigated with Swine Wastewater of Jiangxi Province, China.

The rapid development of livestock and poultry industry in China has caused serious environment pollution problems. To understand the heavy metals accumulation and identify their sources, 7 heavy metals contents and lead isotope ratios were determined in 24 soil samples from vegetable fields irrigated with swine wastewater in Dongxiang County, Jiangxi Province, China. The results showed that the concentration of Cr, Ni, Cu, Zn, As, Cd and Pb in the swine wastewater irrigated vegetable soils varied from 38.5 to 86.4, 7.57 to 30.6, 20.0 to 57.1, 37.5 to 174, 9.18 to 53.1, 0.043 to 0.274 and 12.8 to 37.1 mg/kg, respectively. The soils were moderately to heavily polluted by As, moderately polluted by Cr, Ni, Cu, Zn and Cd, and unpolluted to moderately polluted by Pb. Sampling soils were classified as moderately polluted according to the Nemerow comprehensive pollution index. Lead isotope and Principal Component Analysis (PCA) analysis indicated that swine wastewater irrigation and atmospheric deposition were the primary sources of the heavy metals.

Mining tailings alter insects: revealing fluctuating asymmetry in the caddisfly Smicridea coronata.

The Samarco/Vale/BHP mine tailing dam breach that took place in Minas Gerais, southeastern Brazil, in 2015, deposited high concentrations of metals and metalloids in the Rio Doce basin, severely impacting freshwater and riverine forest ecosystems. To assess developmental instability of caddisflies in response to the environmental impacts of the dam breach, we investigated the fluctuating asymmetry (FA) in the species Smicridea (Rhyacophylax) coronata (Trichoptera: Hydropsychidae). FA was assessed at individual and populational scales using geometric morphometric methods in the cephalic capsule and mandibles of larvae and also on the forewings of adults, both collected under the impacted condition, and under the least disturbed condition. The levels of FA increased in response to stressors on the forewings at the populational scale, and on the mandibles, at individual scale. These morphological variations in the larval and adult stages may lead to detrimental effects and result in high mortality rates as well as lower adult fitness. Trichoptera forewings are revealed as suitable traits for assessing FA, holding potential for applications in biomonitoring programs. Directional asymmetry levels were higher than FA levels for all traits, and this correlation could be explained by a transition from fluctuating to directional asymmetry in the presence of heightened disturbance. Our results validate the relationship between the impacts from the dam breach and increased developmental instability in this species with likely cascade effects on the insect community.

Analysing district-level climate vulnerability pattern in Madhya Pradesh, India, based on agricultural and socio-economic indicators.

Indian agriculture transitioned from a food deficit sector to a food surplus following the Green Revolution. However, the continued progress of Indian agriculture has been hampered by climate change. This research explores the district-wise vulnerability in Madhya Pradesh, India, to climate change by assessing the composite vulnerability index using the agricultural vulnerability index (AVI) and socio-economic vulnerability index (SEVI). The study seeks to understand how agricultural and socio-economic factors lead to variations in vulnerability across districts and influence targeted adaptation and mitigation strategies. The trend analysis results present declining rainfall and inclining temperature from 1951 to 2021 in Madhya Pradesh, directly affecting the agricultural sector and human livelihood. The composite vulnerability index (CVI) results revealed that districts with low values (< 0.394), such as Burhanpur and Balaghat, demonstrate reduced susceptibility due to limited cultivation, low reliance on rainfall, lower drought susceptibility, and decreased population density. Districts such as Panna and Bhopal show moderate vulnerability (0.394-0.423), with lower fallow land, reduced rainfed agriculture, and socio-economic vulnerability. Extensive agriculture and marginalised workers' presence influence high vulnerability (0.423 to 0.456) in districts such as Tikamgarh and Indore. Districts like Barwani and Jhabua have the highest CVI values (> 0.456), indicating substantial susceptibility to climate impacts. The cluster analysis validates the results of the vulnerability index. The findings highlight the urgent need for tailored adaptation strategies to address the diverse agricultural and socio-economic indicators creating vulnerability in Madhya Pradesh. The study helps understand regional vulnerability patterns and provides evidence-based policy approaches for resilience to climate change effects.

Global trends of dioxin and dioxin-like PCBs in animal-origin foods: a systematic review and gap areas.

Dioxins and dioxin-like polychlorinated biphenyls are a group of lipophilic compounds classified under persistent environmental pollutants (POPs). Significant sources of dioxin emissions include industrial effluents, open burning practices, and biomedical and municipal waste incinerators. These emissions will enter the food chain and accumulate in animal-origin foods (AOFs). A systematic review was conducted to analyze the global levels of dioxins and dioxin-like PCBs in AOFs using PRISMA guidelines 2020. The data on the dioxin contamination in AOFs were extracted from 53 publications based on their presence in eggs, meat and meat products, milk and dairy products, marine fish and fish products, and freshwater fish and crabs. A gap analysis was conducted based on the systematic review to understand the grey areas to be focused on the  future. No trend of dioxin contamination in AOFs was observed. A significant gap area was found in the need for nationwide data generation in countries without periodic monitoring of AOFs for dioxin contamination. Source apportionment studies need to be explored for the dioxin contamination of AOFs. Large-scale screening tests of AOFs using DR-CALUX based on market surveys are required for data generation. The outcomes of the study will be helpful for stakeholders and policyholders in framing new policies and guidelines for food safety in AOFs.

Detection and modeling of Staphylococcus aureus and fecal bacteria in Hawaiian coastal waters and sands.

Microbial pollution of recreational waters leads to millions of skin, respiratory, and gastrointestinal illnesses globally. Fecal indicator bacteria (FIB) are monitored to assess recreational waters but may not reflect the presence of Staphylococcus aureus, a global leader in bacterial fatalities. Since many community-acquired S. aureus skin infections are associated with high recreational water usage, this study measured and modeled S. aureus, methicillin-resistant S. aureus (MRSA), and FIB (Enterococcus spp., Clostridium perfringens) concentrations in seawater and sand at six beaches in Hilo, Hawai'i, USA, over 37 sample dates from July 2016 to February 2019 using culturing techniques. Generalized linear models predicted bacterial concentrations with physicochemical and environmental data. Beach visitors were also surveyed on their preferred activities. S. aureus and FIB concentrations were roughly 6-78 times higher at beaches with freshwater discharge than at those without. Seawater concentrations of Enterococcus spp. were positively associated with MRSA but not S. aureus. Elevated S. aureus was associated with lower tidal heights, higher freshwater discharge, onsite sewage disposal system density, and turbidity. Regular monitoring of beaches with freshwater input, utilizing real-time water quality measurements with robust modeling techniques, and raising awareness among recreational water users may mitigate exposure to S. aureus, MRSA, and FIB. PRACTITIONER POINTS: Staphylococcus aureus and fecal bacteria concentrations were higher in seawater and sand at beaches with freshwater discharge. In seawater, Enterococcus spp. positively correlated with MRSA, but not S. aureus. Freshwater discharge, OSDS density, water turbidity, and tides significantly predicted bacterial concentrations in seawater and sand. Predictive bacterial models based upon physicochemical and environmental data developed in this study are readily available for user-friendly application.

Influence of meteorological variables and air pollutants on fog/smog formation in seven major cities of Indo-Gangetic Plain.

The Indo-Gangetic Plains (IGP) of the Indian subcontinent during winters experience widespread fog episodes. The low visibility is not only attributed to meteorological conditions but also to the increased pollution levels in the region. The study was carried out for Tier 1 and Tier II cities of the IGP of India, including Kolkata, Amritsar, Patiala, Hisar, Delhi, Patna, and Lucknow. This work analyzes data from 1990 to 2023 (33 years) employing the Mann-Kendall-Theil-Sen slope to determine the trends in fog occurrences and the relation between fog and meteorological parameters using multiple linear regressions. Furthermore, identifying the most relevant fog (visibility)-impacting factors from a set of both meteorological factors and air pollutants using step-wise regression. All cities indicated trend in the number of foggy days except for Kolkata. The multiple regression analysis reveals relatively low associations between fog occurrences and meteorological factors (30 to 59%), although the association was stronger when air pollution levels were considered (60 to 91%). Relative humidity, PM2.5, and PM10 have the most influence on fog formation. The study provides comprehensive insights into fog trends by incorporating meteorological data and air pollution analysis. The findings highlight the significance of acknowledging meteorological and pollution factors to understand and mitigate the impacts of reduced visibility. Hence, this information can guide policymakers, urban planners, and environmental management agencies in developing effective strategies to manage fog-related risks and improve air quality.

Influence of land use change on habitat quality: a case study of coal mining subsidence areas.

Revealing the spatiotemporal evolution characteristics and key driving processes behind the habitat quality is of great significance for the scientific management of production, living, and ecological spaces in resource-based cities, as well as for the efficient allocation of resources. Focusing on the largest coal-mining subsidence area in Jiangsu Province of China, this study examines the spatiotemporal evolution of land use intensity, morphology, and functionality across different time periods. It evaluates the habitat quality characteristics of the Pan'an Lake area by utilizing the InVEST model, spatial autocorrelation, and hotspot analysis techniques. Subsequently, by employing the GTWR model, it quantifies the influence of key factors, unveiling the spatially varying characteristics of their impact on habitat quality. The findings reveal a notable surge in construction activity within the Pan'an Lake area, indicative of pronounced human intervention. Concurrently, habitat degradation intensifies, alongside an expanding spatial heterogeneity in degradation levels. The worst habitat quality occurs during the periods of coal mining and large-scale urban construction. The escalation in land use intensity emerges as the primary catalyst for habitat quality decline in the Pan'an Lake area, with other factors exhibiting spatial variability in their effects and intensities across different stages.

Comparison of numerical calculations and ALOHA modeling in consequence assessment of chlorine gas emissions from ethylene dichloride reactors.

Incidents involving chemical storage tanks in the petrochemical industry are significant events with severe consequences. Within the petrochemical industry, EDC is a sector that produces ethylene dichloride through the reaction of chlorine and ethylene. The present research was conducted to evaluate the consequences of chlorine gas released from the EDC reactor in a petrochemical industry in southern Iran. Data regarding reactor specifications were obtained from the factory's technical office, while climatic data was acquired from the Meteorological Organization. The consequences of chlorine gas release from the reactor were assessed in four predefined scenarios using numerical calculation methods and modeling with the ALOHA software. The numerical calculation method involved thermodynamic fluid path analysis, discharge coefficient calculations, and wind speed impact analysis. The hazard radius was determined based on the ERPG1-2-3 index. Results showed that in the scenario of chlorine gas release from EDC reactors, according to the ALOHA model, an increase in wind speed from 3 to 7 m/h led to an expanded dispersion radius. At a radius of 700 m from the reactor, the maximum outdoor concentration reached 3.12 ppm, decreasing to 2.27 ppm at 800 m and further to 1.53 ppm at 1000 m. The comparison of numerical calculations and modeling using the ALOHA software indicates the desirable conformity of the results with each other. The R2 coefficient for evaluating the conformity of the results was 0.9964, indicating the desired efficiency of the model in evaluating the consequences of the release of toxic gasses from the EDC tank. The results of this research can be useful in designing the site and emergency response plan.

Multispectral analysis-ready satellite data for three East African mountain ecosystems.

The East African mountain ecosystems are facing increasing threats due to global change, putting their unique socio-ecological systems at risk. To monitor and understand these changes, researchers and stakeholders require accessible analysis-ready remote sensing data. Although satellite data is available for many applications, it often lacks accurate geometric orientation and has extensive cloud cover. This can generate misleading results and make it unreliable for time-series analysis. Therefore, it needs comprehensive processing before usage, which encompasses multi-step operations, requiring large computational and storage capacities, as well as expert knowledge. Here, we provide high-quality, atmospherically corrected, and cloud-free analysis-ready Sentinel-2 imagery for the Bale Mountains (Ethiopia), Mounts Kilimanjaro and Meru (Tanzania) ecosystems in East Africa. Our dataset ranges from 2017 to 2021 and is provided as monthly and annual aggregated products together with 24 spectral indices. Our dataset enables researchers and stakeholders to conduct immediate and impactful analyses. These applications can include vegetation mapping, wildlife habitat assessment, land cover change detection, ecosystem monitoring, and climate change research.