How hydrological components of urban blue space influence the thermal milieu?

Present study examines the possible improvement of thermal discomfort mitigation. Unlike prior researches, which focused primarily on cooling effects of urban blue space, this study, instead of physical presence of blue space considers its hydrological components. The aim of the study is to better understand the role hydrological components like water consistency depth etc. In temperature regulation. The work uses field surveys and modeling to demonstrate how these hydrological factors influence the cooling effect of blue space, providing insights on urban thermal management. To fulfill the purpose, spatial association of hydrological components blue space with its thermal environment and cooling effects was assessed. The control of hydrological components on the surrounding air temperature was examined by conducting case studies. RESULTS: reveals greater hydro-duration, deeper water, and higher Water Presence Frequency (WPF) produce greater cooling effects. The study demonstrates a favorable correlation between hydrological richness and temperature reduction. The study also analyzes how land use and wetland size affect temperature, emphasizing the significance of hydrological conservation and restoration for successful temperature mitigation. Due to their hydrology, larger wetlands are able to moderate temperature to some extent, whereas smaller, fragmented wetlands being hydrologically poor are not so influential in this regard. With these results, the present study reaches beyond to the general understanding regarding the cooling effects of the urban blue spaces. While the previous studies primarily focused on estimating the cooling effect of urban blue space, the current one shows its synchronization with the hydrological characteristics. Novelty also entrusts here, through the modeling and field survey current study demonstrates deeper and consistent water coverage in the urban blue space for maximum period of a year pronounces the cooling effect. In addition, in this cooling effect, the role of land use which is a strong determinant of many aspects of the urban environment is also highlighted. Since all these findings define specific hydrological feature, the study has several practical implications. Mare restoration of urban blue space is not enough to mitigate the thermal discomfort. In order to optimize the cooling effect, the conservation of the hydrological richness is essential. The hydrological richness of the smaller wetlands and the edge of the larger wetlands is to be improved. The connection of these wetlands with the adjacent mighty may strengthen the hydrology. The vegetation was found to promote the cooling effect whereas shorter building helped in spreading the cooling effect. Such finding drives to incorporate the blue space with the green infrastructure along with restricting the building height atleast at the edge of the blue space.

How far the types and wetland hydrological conditions influence its provisioning services in the Indian mature Ganges delta.

Present work intended to explore how far the Provisioning Service Value (PSV) of the mature Ganges deltaic wetlands is determined by its typology and a few physical attributes like hydrology and aquatic vegetations. Firstly, a field investigation was carried out in the representative sample sites, and field-measured PSV was calibrated with wetland types, hydrological security, and aquatic plant biomass to perform spatial estimation and mapping of PSV. The estimation yielded average annual PSV of entire wetlands as 146.5 × 105 Indian Rupee (INR)/km2/year, with the highest over bheries (embankments for fish and shrimp aquaculture) 176 × 105 INR/km2/year and lowest over marshy wetlands 107 × 105 INR/km2/year. Sensitivity analysis of this estimation showed in cases of 55% field visited sites, the field-measured PSV was outside the range of low standard regression residuals (-0.5 to 0.5). While searching for the reason behind such error in the estimation, the variability of the field-measured PSV was measured. Various inequality measures showed high inequality in inter and intra-hydrological conditions of the wetland. Analysis of variance (ANOVA) proved statistical significance of within-class variability. To explain the variability of PSV, Kernel Density Estimation (KDE) plotting was performed, incorporating a few other regional conditioning factors like wetland size, fish and shrimp aquaculture, perenniality, expenditure, and external feeding from the experience of the field. From this excesize, external feeding and expenditure were essential factors that should be incorporated along with the wetland characteristics and physical attributes for accurate estimation. Since producing spatial data layers of these factors with a finer resolution is difficult, the study suggests case-specific estimation of PSV instead of general spatial mapping.

How far spatial resolution affects the ensemble machine learning based flood susceptibility prediction in data sparse region.

Although the effect of digital elevation model (DEM) and its spatial resolution on flood simulation modeling has been well studied, the effect of coarse and finer resolution image and DEM data on machine learning ensemble flood susceptibility prediction has not been investigated, particularly in data sparse conditions. The present work was, therefore, to investigate the performance of the resolution effects, such as coarse (Landsat and SRTM) and high (Sentinel-2 and ALOS PALSAR) resolution data on the flood susceptible models. Another motive of this study was to construct very high precision and robust flood susceptible models using standalone and ensemble machine learning algorithms. In the present study, fifteen flood conditioning parameters were generated from both coarse and high resolution datasets. Then, the ANN-multilayer perceptron (MLP), random forest (RF), bagging (B)-MLP, B-gaussian processes (B-GP) and B-SMOreg algorithms were used to integrate the flood conditioning parameters for generating the flood susceptible models. Furthermore, the influence of flood conditioning parameters on the modelling of flood susceptibility was investigated by proposing an ROC based sensitivity analysis. The validation of flood susceptibility models is also another challenge. In the present study, we proposed an index of flood vulnerability model to validate flood susceptibility models along with conventional statistical techniques, such as the ROC curve. Results showed that the coarse resolution based flood susceptibility MLP model has appeared as the best model (area under curve: 0.94) and it has predicted 11.65 % of the area as very high flood susceptible zones (FSz), followed by RF, B-MLP, B-GP, and B-SMOreg. Similarly, the high resolution based flood susceptibility model using MLP has predicted 19.34 % of areas as very high flood susceptible zones, followed by RF (14.32 %),B-MLP (14.88 %), B-GP, and B-SMOreg. On the other hand, ROC based sensitivity analysis showed that elevation influences flood susceptibility largely for coarse and high resolution based models, followed by drainage densityand flow accumulation. In addition, the accuracy assessment using the IFV model revealed that the MLP model outperformed all other models in the case of a high resolution imageThe coarser resolution image's performance level is acceptable but quite low. So, the study recommended the use of high resolution images for developing a machine learning algorithm based flood susceptibility model. As the study has clearly identified the areas of higher flood susceptibility and the dominant influencing factors for flooding, this could be used as a good database for flood management.

Effects of lockdown due to COVID-19 outbreak on air quality and anthropogenic heat in an industrial belt of India.

Highly urbanized and industrialized Asansol Durgapur industrial belt of Eastern India is characterized by severe heat island effect and high pollution level leading to human discomfort and even health problems. However, COVID-19 persuaded lockdown emergency in India led to shut-down of the industries, traffic system, and day-to-day normal work and expectedly caused changes in air quality and weather. The present work intended to examine the impact of lockdown on air quality, land surface temperature (LST), and anthropogenic heat flux (AHF) of Asansol Durgapur industrial belt. Satellite images and daily data of the Central Pollution Control Board (CPCB) were used for analyzing the spatial scale and numerical change of air quality from pre to amid lockdown conditions in the study region. Results exhibited that, in consequence of lockdown, LST reduced by 4.02 °C, PM10 level decreased from 102 to 18 µg/m3 and AHF declined from 116 to 40W/m2 during lockdown period. Qualitative upgradation of air quality index (AQI) from poor to very poor state to moderate to satisfactory state was observed during lockdown period. To regulate air quality and climate change, many steps were taken at global and regional scales, but no fruitful outcome was received yet. Such lockdown (temporarily) is against economic growth, but it showed some healing effect of air quality standard.

Modelling water richness and habitat suitability of the wetlands and measuring their spatial linkages in mature Ganges delta of India.

Present study has attempted to measure Water Richness (WR) and Wetland Habitat Suitability (WHS) in deltaic environment and assessed their spatial linkages. Water richness exhibits availability of water in wetland and its dynamicity, whereas wetland habitat suitability depicts physical habitat ambiance of a wetland toward vibrant ecosystem. Both the components are very essential and should be measured to explore ecosystem service and environmental heath of a region. For investigating water richness of the wetland six water availability indicating parameters have been chosen and for assessing wetland habitat suitability four additional parameters have been taken into consideration. Four widely used and recognised machine learning algorithms like Reduced Error Pruning (REP) tree, Random forest, Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been employed here in order to develop suitable model at two phases. Results reveal that very high water rich zone is found over 200-215 km2 wetland area followed by high water rich zone over 125-140 km2 wetland area in both the phases. Wetland habitat suitability assessment shows only 100-150 km2 of the wetland having very high suitability and 110-120 km2 of wetland having high suitability. Field investigation and accuracy assessment support the validity and acceptability of the results. Spatial linkage between water richness and habitat suitability demonstrates that 30-40% very high water rich zone represents very high habitat suitability figuring out importance of both the models. Therefore, results recommend that only water richness of the wetlands of the wetlands is not enough to represent the habitat suitability in the densely populated riparian flood plain region.

Effect of channel morphological changes on wetland transformation.

Keeping aside the traditional approaches to investigating floodplain wetland transformation, the current study investigated various aspects of it through changes in river channel morphology and drainage pattern. The study analyzed wetland transformation using satellite image-based machine learning and intensive fieldwork. Ordinary Least Square (OLS) regression was applied to identify dominant influencing factors among 24 contributing factors under six clusters to eight dependent phenomena of transformation. The result showed that 57 % of wetland area lost since 1991, and existing wetland has also experiencing hydrological scarcity. From 1991 to 2021, the area under low water depth (<1 m.) inflated from 18.55 % to 50.54 %, the hydro-period narrowed down, and the appearance of water become inconsistent. The OLS result showed that changes in channel morphology (bottle neck channel, embankment-driven carrying capacity enhancement, etc.), interruptions in river and wetland connecting channels (source closure, breaching the continuity, conversion in to agricultural land, etc.), and changes in flood ambience (regulated by dam construction, erection of embankments, etc.) majorly contributed to wetland transformation. Very high explainability was found in the cases of rate of wetland loss, decreasing water depth under greater depth, narrowing hydro-period (R2 > 0.9). The findings of this work would be a good policy document for floodplain wetland management.

Ecological consequences of urban blue space transformation.

This study presents the ecological consequences of the blue space conversion and its qualitative degradation in the English Bazar Municipality (EBM) and its surrounding area. The primary blue spaces of the area, the marshy wetland called Chatra and Mohananda river, are the most affected due to urban activities like built-up expansion and sewage and wastewater discharge. Built-up development encroached more than 300 m within wetland territory and caused a 0.57 km2 conversion of wetland area. It is also evident within the bed of the Mohananda river. Agriculture also caused the conversion of the blue space. As a result, the wetland's ecosystem service value (ESV) was reduced by 12.7%, along with a reduction of cultural services by 27.86%. The massive pouring of sewage and wastewater caused hyper-eutrophication in almost the entire wetland area. The trophic state index (TSI) value increased significantly in the last 10 years, causing high growth and areal expansion of water hyacinth. The expanding settlements and agricultural land that captured the river channel face inundation vulnerability during peak discharge. Extreme danger level discharge causes floods in the extensive municipality area. The areal encroachment, water extraction, sewage and wastewater discharge, and water quality deterioration caused severe hydro-ecological degradation of the river. Since blue space is critically essential for urban environmental health, these ecological consequences can cause a crisis for urban wellbeing. Therefore, the anthropogenic adversities towards the urban blue space must be restricted, and the blue space's ecological sustenance must be paid enough attention.

Methane emissions only negligibly reduce the ecosystem service value of wetlands and rice paddies in the mature Ganges Delta.

Wetland provides a wide range of ecosystem services with immense value. However, methane (CH4) emissions adversely affect ecosystem services, and it requires fixation cost. The objective of the present study was to estimate CH4 emissions and ecosystem services value (ESV) and how much the fixation cost of CH4 reduces the ESV. Since rice cultivation is a very common practice here, the paddy fields were also incorporated in this study. CH4 flux and satellite data were employed for estimating the emissions with the help of two-factor (temperature and water availability) model. Global coefficients of ecosystem service value (ESV) that is defined as the monetary valuation of materialistic and non-materialistic services were adapted for estimating the ecosystem service of the CH4 emitting sources. Results show that during the boro season (pre-monsoon summer paddy cultivation season), average monthly emissions of paddy fields are equal to the wetlands which are 0.16 t/km2. During amon season (monsoon paddy cultivation season), this emissions is 0.7 t/km2 and 0.53 t/km2, respectively, from wetlands and paddy fields. Both wetlands and paddy fields emit a greater amount of CH4 during amon season than boro season. Behind this seasonal variation, water availability in terms of precipitation-evaporation ratio plays a more vital role than temperature. Total estimated ESV is 928.51 million US$, and CH4 fixation cost is 6.64 million US$ which is only 0.71% to total ESV. So, considering such huge net ESV, emphasis on wetland conservation and restoration are necessary.

Assessing the role of deltaic flood plain wetlands on regulating methane and carbon balance.

Present study deals with the role of wetland for regulating greenhouse gases (GHG) particularly methane (CH4) emission and carbon (C) sequestration in mature Ganges deltaic environment. The annual total amount of emission and sequestration in wetlands of varying types was estimated along with the seasonal variation. Result showed that the streams were the highest emitter of CH4 followed by ox-bow lakes in all the seasons whereas the bheries (embanked pisciculture arresting tidal water) consistently exhibited the lowest average emission. The average sequestration of C was the highest in ox-bow lakes followed by marshes and mudflats. The average emission in monsoon season was 43% and 78% higher than the average emission of pre and post-monsoon seasons respectively. The yearly total emission was 8.01 × 103 ton and yearly total sequestration was estimated 908.98 × 103 ton. From the perspective of GHG regulation, the wetlands were found to yearly uptake four times higher carbon dioxide (CO2) than the CO2 equivalent (CO2e) of emitted CH4. After offsetting the fixation cost of emitted CH4, the yearly surplus sequestrated C in the wetlands of the entire region was worthy of 68.46 million US dollar (USD). So, wetland plays positive role for reducing greenhouse gas effect and associated temperature rise which is considered to be serious issue. Such result has made a good agreement on the debated issue of wetland CH4 emission and C sequestration and will encourage restoring wetland for even mediating GHG issue.

Identifying sensitivity of factor cluster based gully erosion susceptibility models.

The present study has attempted to address the issue of sensitivity of different clusters of factors towards gully erosion in the Mayurakshi river basin. Firstly, the gully erosion susceptibility of the basin area has been mapped by integrating using 18 parameters divided into four factor-cluster, viz. erodibility, erosivity, resistance, and topographical cluster, with the help of four machine learning (ML) models such as random forest (RF), gradient boost (GBM), extreme gradient boost (XGB), and support vector machine (SVM). Results show that almost 20% and 25% of the upper catchment of the basin belongs to extreme and high gully erosion susceptibility. Among the applied algorithms, RF is appeared as the best performing model. The spatial association of factor cluster-based models with the final susceptibility model is found the highest for the erosivity cluster, followed by the erodibility cluster. From the sensitivity analysis, it becomes clear that geology and soil texture are dominant contributing factors to gully erosion susceptibility. The geological formation of unclassified granite gneiss and geomorphological formation of denudational origin pediment-pediplain complex is dominant over the entire upper catchment of the basin, and therefore, can be considered regional factors of importance. Since the study has figured out the different grades of susceptible areas with dominant factors and factor cluster, it would be useful for devising planning for gully erosion check measures. From economic particularly food security purpose, it is very essential since it is concerned with precious soil loss and negative effects on agriculture.

Machine learning models for wetland habitat vulnerability in mature Ganges delta.

The present study attempts to measure wetland habitat vulnerability (WHV) in the Indian part of mature Ganges delta. Predictive algorithms belonging to bivariate statistics and machine learning (ML) algorithms were applied for fulfilling the data mining and generating the models. Results show that 60% of the wetland areas are covered by moderate to very high WHV, out of which > 300 km2 belong to very high WHV followed by a high vulnerability in almost 150 km2. This areal coverage increases by 10-15% from phase II to phase III. On the other hand, a relatively safe situation is confined to < 200 km2. The receiver operating characteristic curve, root-mean-square error, and correlation coefficient are used to assess the accuracy of these models and categorization of habitat vulnerability. Ensemble modeling is done using the individual models having a greater accuracy level in order to increase accuracy. A field-based model of the same is prepared by gathering information directly from the field which also exhibits similar results with the algorithm-based models. Analysis of residuals in standard regression strongly supports the relevance of the selected parameters and multi-parametric models.

How far climatic parameters associated with air quality induced risk state (AQiRS) during COVID-19 persuaded lockdown in India.

Global temperature rises in response to accumulating greenhouse gases is a well-debated issue in the present time. Historical records show that greenhouse gases positively influence temperature. Lockdown incident has brought an opportunity to justify the relation between greenhouse gas centric air pollutants and climatic variables considering a concise period. The present work has intended to explore the trend of air quality parameters, and air quality induced risk state since pre to during the lockdown period in reference to India and justifies the influence of pollutant parameters on climatic variables. Results showed that after implementation of lockdown, about 70% area experienced air quality improvement during the lockdown. The hazardous area was reduced from 7.52% to 5.17%. The spatial association between air quality components and climatic variables were not found very strong in all the cases. Still, statistically, a significant relation was observed in the case of surface pressure and moisture. From this, it can be stated that pollutant components can control the climatic components. This study recommends that pollution source management could be a partially good step for bringing climatic resilience of a region.