Unveiling urban air quality dynamics during COVID-19: a Sentinel-5P TROPOMI hotspot analysis.

In India, the spatial coverage of air pollution data is not homogeneous due to the regionally restricted number of monitoring stations. In a such situation, utilising satellite data might greatly influence choices aimed at enhancing the environment. It is essential to estimate significant air contaminants, comprehend their health impacts, and anticipate air quality to safeguard public health from dangerous pollutants. The current study intends to investigate the spatial and temporal heterogeneity of important air pollutants, such as sulphur dioxide, nitrogen dioxide, carbon monoxide, and ozone, utilising Sentinel-5P TROPOMI satellite images. A comprehensive spatiotemporal analysis of air quality was conducted for the entire country with a special focus on five metro cities from 2019 to 2022, encompassing the pre-COVID-19, during-COVID-19, and current scenarios. Seasonal research revealed that air pollutant concentrations are highest in the winter, followed by the summer and monsoon, with the exception of ozone. Ozone had the greatest concentrations throughout the summer season. The analysis has revealed that NO2 hotspots are predominantly located in megacities, while SO2 hotspots are associated with industrial clusters. Delhi exhibits high levels of NO2 pollution, while Kolkata is highly affected by SO2 pollution compared to other major cities. Notably, there was an 11% increase in SO2 concentrations in Kolkata and a 20% increase in NO2 concentrations in Delhi from 2019 to 2022. The COVID-19 lockdown saw significant drops in NO2 concentrations in 2020; specifically, - 20% in Mumbai, - 18% in Delhi, - 14% in Kolkata, - 12% in Chennai, and - 15% in Hyderabad. This study provides valuable insights into the seasonal, monthly, and yearly behaviour of pollutants and offers a novel approach for hotspot analysis, aiding in the identification of major air pollution sources. The results offer valuable insights for developing effective strategies to tackle air pollution, safeguard public health, and improve the overall environmental quality in India. The study underscores the importance of satellite data analysis and presents a comprehensive assessment of the impact of the shutdown on air quality, laying the groundwork for evidence-based decision-making and long-term pollution mitigation efforts.

Quantitative assessment of interplay between urbanization dynamics and land surface temperature variations using generalized additive model coupled PDP for sustainable urban planning and management.

The mountainous region of Asir is experiencing rapid and unsystematic urbanization leading to an increase in land surface temperatures (LST), which poses a challenge to human well-being and ecological balance. Therefore, it is necessary to study the interaction between land use and land cover (LULC)-induced urbanization and LST using advanced geostatistical techniques. In addition, understanding the urbanization process and urban density is essential for effective urban planning and management. The aim of this study was to investigate the interaction between the urbanization process, urban density and the associated LST. Using the Random Forest Algorithm, LULC mapping was conducted for the years 1990, 2000 and 2020. Metrics such as land cover change rate (LCCR), land cover index (LCI), landscape expansion index (LEI), mean landscape expansion index (MLEI) and area-weighted landscape expansion index (AWLEI) were used to understand urbanization processes and LULC changes. Convolutional kernels were used to model urban density, and the mono-window algorithm was applied to analyse LST in the selected years. In addition, the study assessed the Surface Urban Heat Island (SUHI) contribution index to LULC and used Generalized Additive Models (GAMs) in conjunction with Partial Dependence Plots (PDPs) to understand the relationship between urbanization processes, urban density and LST. In a detailed 30-year study, the application of the RF algorithm showed significant shifts in LULC with an overall validation accuracy of over 85%. Urban areas grew dramatically from 69.40 km2 in 1990 to 338.74 km2 in 2020, while water areas decreased from 1.51 to 0.54 km2. Dense vegetation increased from 43.36 to 52.22 km2, indicating positive ecological trends. The LST analysis showed a general warming, with the mean LST increasing from 40.51 °C in 1990 to 46.73 °C in 2020 and the highest temperature category (50-60 °C) increasing from 0.78 to 33.35 km2. The built-up area of cities tripled between 1990 and 2020, with the Landscape Expansion Index reflecting significant growth in suburban areas. The modeling of urban density shows increasing urbanization in the centre, which will expand significantly to the east by 2020. The contribution of LULC to LST and the Urban Heat Island (SUHI) effect was evident, with built-up areas showing a constant temperature increase. GAMs confirmed a statistically significant relationship between urban density and LST, with different effects for different types of urban expansion. This comprehensive study quantitatively sheds light on the complicated dynamics of urbanization, land cover change and temperature variation and provides important insights for sustainable urban development.

Evaluating the impact of urbanization on the urban heat islands through integrated radius and non-linear regression approach.

Urban heat islands (UHIs) are a significant environmental problem, exacerbating the urban climate and affecting human health in the Asir region of Saudi Arabia. The need to understand the spatio-temporal dynamics of UHI in the context of urban expansion is crucial for sustainable urban planning. The aim of this study was to quantify the changes in land use and land cover (LULC) and urbanization, assess the expansion process of UHI, and analyze its connectivity in order to develop strategies to mitigate UHI in an urban context over a 30-year period from 1990 to 2020. Using remote sensing data, LULC changes were analyzed with a random forest model. LULC change rate (LCCR), land cover intensity (LCI), and landscape expansion index (LEI) were calculated to quantify urbanization. The land surface temperature for the study period was calculated using the mono-window algorithm. The UHI effect was analyzed using an integrated radius and non-linear regression approach, fitting SUHI data to polynomial curves and identifying turning points based on the regression derivative for UHI intensity belts to quantify the expansion and intensification of UHI. Landscape metrics such as the aggregation index (AI), landscape shape index (LSI), and four other matrices were calculated to assess UHI morphology and connectivity of the UHI. In addition, the LEI was adopted to measure the extent of UHI growth patterns. From 1990 to 2020, the study area experienced significant urbanization, with the built-up area increasing from 69.40 to 338.74 km2, an increase of 1.923 to 9.385% of the total area. This expansion included growth in peripheral areas of 129.33 km2, peripheral expansion of 85.40 km2, and infilling of 3.80 km2. At the same time, the UHI effect intensified with an increase in mean LST from 40.55 to 46.73 °C. The spatial extent of the UHI increased, as shown by the increase in areas with an LST above 50 °C from 36.58 km2 in 1990 to 133.52 km2 in 2020. The connectivity of the UHI also increased, as shown by the increase in the AI from 38.91 to 41.30 and the LSI from 56.72 to 93.64, reflecting a more irregular and fragmented urban landscape. In parallel to these urban changes, the area classified as UHI increased significantly, with the peripheral areas expanding from 23.99 km2 in the period 1990-2000 to 80.86 km2 in the period 2000-2020. Peripheral areas also grew significantly from 36.42 to 96.27 km2, contributing to an overall more pronounced and interconnected UHI effect by 2020. This study provides a comprehensive analysis of urban expansion and its thermal impacts. It highlights the need for integrated urban planning that includes strategies to mitigate the UHI effect, such as improving green infrastructure, optimizing land use, and improving urban design to counteract the negative effects of urbanization.

Spatiotemporal assessment of the nexus between urban sprawl and land surface temperature as microclimatic effect: implications for urban planning.

Rapid urbanisation has led to significant environmental and climatic changes worldwide, especially in urban heat islands where increased land surface temperature (LST) poses a major challenge to sustainable urban living. In the city of Abha in southwestern Saudi Arabia, a region experiencing rapid urban growth, the impact of such expansion on LST and the resulting microclimatic changes are still poorly understood. This study aims to explore the dynamics of urban sprawl and its direct impact on LST to provide important insights for urban planning and climate change mitigation strategies. Using the random forest (RF) algorithm optimised for land use and land cover (LULC) mapping, LULC models were derived that had an overall accuracy of 87.70%, 86.27% and 93.53% for 1990, 2000 and 2020, respectively. The mono-window algorithm facilitated the derivation of LST, while Markovian transition matrices and spatial linear regression models assessed LULC dynamics and LST trends. Notably, built-up areas grew from 69.40 km2 in 1990 to 338.74 km2 in 2020, while LST in urban areas showed a pronounced warming trend, with temperatures increasing from an average of 43.71 °C in 1990 to 50.46 °C in 2020. Six landscape fragmentation indices were then calculated for urban areas over three decades. The results show that the Largest Patch Index (LPI) increases from 22.78 in 1990 to 65.24 in 2020, and the number of patches (NP) escalates from 2,531 in 1990 to an impressive 10,710 in 2020. Further regression analyses highlighted the morphological changes in the cities and attributed almost 97% of the LST variability to these urban patch dynamics. In addition, water bodies showed a cooling trend with a temperature decrease from 33.76 °C in 2000 to 29.69 °C in 2020, suggesting an anthropogenic influence. The conclusion emphasises the urgent need for sustainable urban planning to counteract the warming trends associated with urban sprawl and promote climate resilience.

Applying deep learning to manage urban ecosystems in arid Abha, Saudi Arabia: Remote sensing-based modelling for ecological condition assessment and decision-making.

This study aims to quantitatively and qualitatively assess the impact of urbanisation on the urban ecosystem in the city of Abha, Saudi Arabia, by analysing land use changes, urbanisation processes and their ecological impacts. Using a multidisciplinary approach, a novel remote sensing-based urban ecological condition index (RSUSEI) will be developed and applied to assess the ecological status of urban surfaces. Therefore, the identification and quantification of urbanisation is important. To do so, we used hyper-tuned artificial neural network (ANN) as well as Land Cover Change Rate (LCCR), Land Cover Index (LCI) and Landscape Expansion Index (LEI). For the development of (RSUSEI), we have used four advanced models such as fuzzy Logic, Principle Component Analysis (PCA), Analytical Hierarchy Process (AHP) and fuzzy Analytical Hierarchy Process (FAHP) to integrate various ecological parameters. In order to obtain more information for better decision making in urban planning, sensitivity and uncertainty analyses based on a deep neural network (DNN) were also used. The results of the study show a multi-layered pattern of urbanisation in Saudi Arabian cities reflected in the LCCR, indicating rapid urban expansion, especially in the built-up areas with an LCCR of 0.112 over the 30-year period, corresponding to a more than four-fold increase in urban land cover. At the same time, the LCI shows a remarkable increase in 'built-up' areas from 3.217% to 13.982%, reflecting the substantial conversion of other land cover types to urban uses. Furthermore, the LEI emphasises the complexity of urban growth. Outward expansion (118.98 km2), Edge-Expansion (95.22 km2) and Infilling (5.00 km2) together paint a picture of a city expanding outwards while filling gaps in the existing urban fabric. The RSUSEI model shows that the zone of extremely poor ecological condition covers an area of 157-250 km2, while the natural zone covers 91-410 km2. The DNN based sensitivity analysis is useful to determine the optimal model, while the integrated models have lower input parameter uncertainty than other models. The results of the study have significant implications for the management of urban ecosystems in arid areas and the protection of natural habitats while improving the quality of life of urban residents. The RSUSEI model can be used effectively to assess urban surface ecology and inform urban management techniques.

Assessment of coastal vulnerability using integrated fuzzy analytical hierarchy process and geospatial technology for effective coastal management.

The vulnerability of coastal regions to climate change is a growing global concern, particularly in Bangladesh, which is vulnerable to flooding and storm surges due to its low-lying coastal areas. In this study, we used the fuzzy analytical hierarchy process (FAHP) method to assess the physical and social vulnerability of the entire coastal areas of Bangladesh, using 10 critical factors to evaluate the coastal vulnerability model (CVM). Our analysis indicates that a significant portion of the coastal regions of Bangladesh is vulnerable to the impacts of climate change. We found that one-third of the study area, encompassing around 13,000 km2, was classified as having high or very high coastal vulnerability. Districts in the central delta region, such as Barguna, Bhola, Noakhali, Patuakhali, and Pirojpur, were found to have high to very high physical vulnerability. Meanwhile, the southern parts of the study area were identified as highly socially vulnerable. Our findings also showed that the coastal areas of Patuakhali, Bhola, Barguna, Satkhira, and Bagerhat were particularly vulnerable to the impacts of climate change. The coastal vulnerability map we developed using the FAHP method showed satisfactory modeling, with an AUC of 0.875. By addressing the physical and social vulnerability factors identified in our study, policymakers can take proactive steps to ensure the safety and wellbeing of coastal residents in the face of climate change.

Assessing landscape fragmentation due to urbanization in English Bazar Municipality, Malda, India, using landscape metrics.

Urbanization in India is having a significant impact on the environment and human health, as built-up areas are expanding while vegetation and water bodies are declining. To mitigate the negative effects of urbanization, there is an urgent need to monitor and manage the growth of cities through the creation of smart cities. Accurately identifying and mapping urban expansion patterns is crucial to achieving this goal, but this is currently lacking in India. To address this gap, this study takes a comprehensive approach to examining the processes of urban expansion in English Bazar Municipality, Malda, India. The study employs the random forest (RF) method for land use and land cover (LULC) mapping for the years 2001, 2011, and 2021 and then uses the frequency approach, landscape homogeneity, and fragmentation to model urban expansion over the same time period. The study also employs several landscape matrices to quantify urban expansion. The results of the study reveal a substantial increase in built-up areas from 601.26 hectares in 2001 to 859.13 hectares in 2021, accompanied by a corresponding decline in vegetation, cropland, and open land. This land use transition process has also led to increased fragmentation of the landscape. The spatiotemporal landscape homogeneity analysis identifies two transition zones (zone 2 and zone 1) surrounding the stable zone, which capture the new, small, isolated built-up areas and show a progression of urban expansion from 2001 to 2021. Overall, the study provides valuable insights into the changes in urban expansion and fragmentation in English Bazar Municipality. To promote sustainable urban development and enhance the quality of life for residents, it is important for stakeholders and the government to prioritize the conservation and creation of green and blue spaces. This can be accomplished through the incorporation of green infrastructure, smart city principles, community engagement and education, and partnerships with local businesses.

Assessment of long-term mangrove distribution using optimised machine learning algorithms and landscape pattern analysis.

Mangrove ecosystems provide numerous benefits, including carbon storage, coastal protection and food for marine organisms. However, mapping and monitoring of mangrove status in some regions, such as the Red Sea area, has been hindered by a lack of data, accurate and precise maps and technical expertise. In this study, an advanced machine learning algorithm was proposed to produce an accurate and precise high-resolution land use map that includes mangroves in the Al Wajh Bank habitat in northeastern Saudi Arabia. To achieve this, high-resolution multispectral images were generated using an image fusion technique, and machine learning algorithms were applied, including artificial neural networks, random forests and support vector machine algorithms. The performance of the models was evaluated using various matrices, and changes in mangrove distribution and connectivity were assessed using the landscape fragmentation model and Getis-Ord statistics. The research gap that this study aims to address is the lack of accurate and precise mapping and assessment of mangrove status in the Red Sea area, particularly in data-scarce regions. Our study produced high-resolution mobile laser scanning (MLS) imagery of 15-m length for 2014 and 2022, and trained 5, 6 and 9 models for artificial neural networks, support vector machines and random forests (RF) to predict land use and land cover maps using 15-m and 30-m resolution MLS images. The best models were identified using error matrices, and it was found that RF outperformed other models. According to the 15-m resolution map of 2022 and the best models of RF, the mangrove cover in the Al Wajh Bank is 27.6 km2, which increased to 34.99 km2 in the case of the 30-m resolution image of 2022, and was 11.94 km2 in 2014, indicating a doubling of the mangrove area. Landscape structure analysis revealed an increase in small core and hotspot areas, which were converted into medium core and very large hotspot areas in 2014. New mangrove areas were identified in the form of patches, edges, potholes and coldspots. The connectivity model showed an increase in connectivity over time, promoting biodiversity. Our study contributes to the promotion of the protection, conservation and planting of mangroves in the Red Sea area.

Mapping and evaluating sustainable and unsustainable urban areas for ecological management towards achieving low-carbon city: an empirical study of Asir Region, Saudi Arabia.

Urbanisation can cause a variety of environmental and health issues, which has prompted experts to evaluate degraded areas and develop management strategies aimed at promoting urban sustainability and reducing carbon emissions. In low-carbon cities, sustainable urban areas have low carbon emission and prioritised carbon reduction by implementing sustainable transportation, green infrastructure, and energy-efficient buildings. On the other hand, unsustainable urban areas tend to lack these priorities and rely heavily on non-renewable energy sources and have high carbon emission. Therefore, this study aims to identify the most sustainable and unsustainable regions in the Abha-Khamis Mushayet Twin City region of Saudi Arabia in respect to urbanisation and carbon emission during the period between 1990 and 2020. To do so, we used Landsat datasets to create land use land cover (LULC) maps and then calculated carbon storage, emission, and absorption using InVest software. Additionally, the study examined micro-climatic conditions by calculating the urban heat island (UHI) effect, which allowed determining sustainable and unsustainable regions by comparing the UHI model and carbon similarity and mismatch model using coupling coordination degree model (CCDM). The study found that during the last three decades, the LULC pattern of the region underwent significant alterations, resulting in a substantial decline in carbon storage from 710,425 Mg C/hm2 in 1990 to approximately 527,012.9 Mg C/hm2 in 2020. Conversely, carbon emissions were observed to be very high in areas with high built-up density, with emission levels exceeding 20 tons per annum. Whilst the areas of excess carbon have decreased significantly, the areas of excess carbon emission have increased over time, resulting in the UHI effect due to high greenhouse gases. By comparing the UHI and carbon similarity and mismatch model, the researchers found that over 280 km2 of the study area is unsustainable and has increased since 1990. In contrast, only about 410 km2 of the study area is currently sustainable. To promote sustainability, the study recommends several strategies such as carbon capture, utilisation, and storage; green infrastructure; and the use of renewable energy to manage carbon emissions.

Modeling the spatiotemporal heterogeneity of land surface temperature and its relationship with land use land cover using geo-statistical techniques and machine learning algorithms.

Rapid changes in land use and land cover (LULC) have ecological and environmental effects in metropolitan areas. Since the 1990s, Saudi Arabia's cities have undergone tremendous urban growth, causing urban heat islands, groundwater depletion, air pollution, loss of ecosystem services, etc. This study evaluates the variance and heterogeneity in land surface temperature (LST) because of LULC changes in Abha-Khamis Mushyet, Saudi Arabia, from 1990 to 2020. The research aims to determine the impact of urban biophysical parameters on the High-High (H-H) LST cluster using geospatial, statistical, and machine learning techniques. The support vector machine (SVM) was used to map LULC. The land surface temperature (LST) has been derived using the mono-window algorithm (MWA). The local indicator of spatial associations (LISA) model was implemented on the spatiotemporal LST maps to identify LST clusters. Also, the parallel coordinate plot (PCP) approach was employed to examine the relationship between LST clusters and urban biophysical variables as a proxy of LULC. LULC maps show that urban areas rose by > 330% between 1990 and 2020. Built-up areas had an 83.6% transitional probability between 1990 and 2020. In addition, vegetation and agricultural land have been transformed into built-up areas by 17.9% and 21.8% respectively between 1990 and 2020. Uneven LULC changes in terms of built-up areas lead to increased LST hotspots. High normalized difference built-up index (NDBI) was linked to LST hotspots but not normalized difference water index (NDWI) or normalized difference vegetation index (NDVI). This research could help policymakers develop mitigation strategies for urban heat islands.

Selecting optimal conditioning parameters for landslide susceptibility: an experimental research on Aqabat Al-Sulbat, Saudi Arabia.

Landslides and other disastrous natural catastrophes jeopardise natural resources, assets, and people's lives. As a result, future resource management will necessitate landslide susceptibility mapping (LSM) using the best conditioning factors. In Aqabat Al-Sulbat, Asir province, Saudi Arabia, the goal of this study was to find optimal conditioning parameters dependent hybrid LSM. LSM was created using machine learning methods such as random forest (RF), logistic regression (LR), and artificial neural network (ANN). To build ensemble models, the LR was combined with RF and ANN models. The receiver operating characteristic (ROC) curve was used to validate the LSMs and determine which models were the best. Then, utilising random forest (RF), classification and regression tree (CART), and correlation feature selection, sensitivity analysis was carried out. Through sensitivity analysis, the most relevant conditioning factors were determined, and the best model was applied to the important parameters to build a highly robust LSM with fewer variables. The ROC curve was also used to evaluate the final model. The results show that two hybrid models (LR-ANN and LR-RF) were predicted the very high as 29.67-32.73 km2 and high LS regions as 21.84-33.38 km2, with LR predicting 22.34km2 as very high and 45.15km2 as high LS zones. The LR-RF appeared as best model (AUC: 0.941), followed by LR-ANN (AUC: 0.915) and LR (AUC: 0.872). Sensitivity analysis, on the other hand, allows for the exclusion of aspects, hillshade, drainage density, curvature, and TWI from LSM. The LSM was then predicted using the LR-RF model based on the remaining nine conditioning factors. With fewer variables, this model has achieved greater accuracy (AUC: 0.927). This comes very close to being the best hybrid model. As a result, it is strongly advised to choose conditioning parameters with caution, as redundant parameters would result in less resilient LSM. As a consequence, both time and resources would be saved, and precise LSM would indeed be possible.