Climate change influences on the streamflow and sediment supply to the Chao Phraya River basin, Thailand.

This study investigates the effects of climate change on the sediment loads of the Ping and Wang River basins and their contribution to the sediment dynamics of the lower Chao Phraya River basin in Thailand. The various climate models under different Representative Concentration Pathways (RCPs) scenarios are employed to project sediment loads in future. The findings indicate a significant increase in river flow approximately 20% in the Ping River (PR) and 35% in the Wang River (WR) by the mid-21st century and continuing into the distant future. Consequently, this is expected to result in sediment loads up to 0.33 × 106 t/y in the PR and 0.28 × 106 t/y in the WR. This escalation is particularly notable under the RCP 8.5 scenario, which assumes higher greenhouse gas emissions. Additionally, the research provides insights into the potential positive implications for the Chao Phraya Delta's coastal management. Without further damming in the Ping and Wang River basins, the anticipated rise in sediment supply could aid in mitigating the adverse effects of land subsidence and sea-level rise, which have historically caused extensive shoreline retreat in the delta region, particularly around Bangkok Metropolis. The paper concludes that proactive adaptation strategies are required to manage the expected changes in the hydrological and sediment regimes to protect vulnerable coastal zones and ensure the sustainable management of the Chao Phraya River Basin in the face of climate change.

Evaluating the effectiveness of Mangrove rehabilitation: A novel approach for sustainable coastal management.

We used UAV-LiDAR technology and other advanced remote sensing techniques to evaluate mangrove rehabilitation projects along the eroding shoreline of the Upper Gulf of Thailand. Our results delineate the necessary biophysical conditions for successfully rehabilitating mangroves, establishing optimal conditions under which mangroves can naturally re-establish and thrive. Furthermore, we investigated the effectiveness of different coastal defense structures in fostering mangrove recolonization. Our analysis shows that nearshore breakwaters markedly outperform submerged breakwaters and bamboo fences, with a success rate of over 65% by significantly reducing wave energy that aids sediment trapping. These findings suggest that refinements in the configuration of coastal structures, including the elevation of breakwater crests and selective deployment of bamboo fences, will enhance mangrove rehabilitation success. These insights affirm the role of UAV-LiDAR surveys for optimizing mangrove restoration initiatives, thereby facilitating sustainable development for coastlines plagued by erosion. The insights gleaned offer a blueprint for bolstering the success rate of mangrove rehabilitation projects, directing them toward sustainable coastal development.

Machine learning approach to evaluate the groundwater quality and human health risk for sustainable drinking and irrigation purposes in South India.

The main purpose of this study was to evaluate the suitability of groundwater for sustainable drinking and irrigation purposes using various indices, such as the nitrate pollution index, agriculture suitability index (ASI), non-carcinogenic human risk assessment (NCHRA), and radial basic function (RBF) model. The novelty of the present study is to develop the ASI model and integrate with RBF model to identify the highly dominating parameter in chemical equilibrium of groundwater. Results showed that >85% of sample locations were suitable for drinking purposes, and the nitrate concentration in groundwater had a negative impact on the overall quality of water. Approximately 12 and 19 sample locations were contaminated owing to the high nitrate concentrations in the study region. The NCHRA study identified that approximately 8.5%, 27.28%, 29.54%, 40.40%, and 28.20% of area was excessively affected during the winter compared to summer season for people 6 to 12 y, 13 to 19 y, 20 to 29 y, 30 to 65 y, and >65 y of age. The RBF model shows that the R2 values for each season were 0.84 and 0.85 during summer and winter, respectively. The north-east and central parts of the study region were found to be more contaminated. The present study identified that, pathway of nitrate contaminant from the agriculture field towards to the sample locations. Overall, parent rock weathering, carbonate ion dissolution, and infiltration of rainwater and leachate from municipal waste dumping yards were the dominant factors influencing the chemical composition of groundwater. The present study achieved the vibrant knowledge about source of contamination, health effect on human body and impact on agriculture uses to develop the cleaner water supply system. The study results will be helpful in enhancing the sustainable action plan for water management in the study area.

A novel approach for the prediction and analysis of daily concentrations of particulate matter using machine learning.

Traditional air quality analysis and prediction methods depend on the statistical and numerical analyses of historical air quality data with more information related to a specific region; therefore, the results are unsatisfactory. In particular, fine particulate matter (PM2.5, PM10) in the atmosphere is a major concern for human health. The modelling (analysis and prediction) of particulate matter concentrations remains unsatisfactory owing to the rapid increase in urbanization and industrialization. In the present study, we reconstructed a prediction model for both PM2.5 and PM10 with varying meteorological conditions (windspeed, temperature, precipitation, specific humidity, and air pressure) in a specific region. In this study, a prediction model was developed for the two observation stations in the study region. The analysis of particulate matter shows that seasonal variation is a primary factor that highly influences air pollutant concentrations in urban regions. Based on historical data, the maximum number of days (92 days in 2019) during the winter season exceeded the maximum permissible level of particulate matter (PM2.5 = 15 µg/m3) concentration in air. The prediction results showed better performance of the Gaussian process regression model, with comparatively larger R2 values and smaller errors than the other models. Based on the analysis and prediction, these novel methods may enhance the accuracy of particulate matter prediction and influence policy- and decision-makers among pollution control authorities to protect air quality.

Effectiveness of grey and green engineered solutions for protecting the low-lying muddy coast of the Chao Phraya Delta, Thailand.

Coastal protection measures can be categorized into grey and green solutions in terms of their ecosystem impacts. As the use of grey solutions has become a serious issue due to environmental consequences during the last few decades, green/nature-based solutions have become prioritized. This study evaluates the effectiveness of grey and green solutions applied along the eastern Chao Phraya Delta (ECPD) based on historical shoreline change analysis and coastal observations using Light Detection and Ranging technology. The results from shoreline analysis indicate that nearshore breakwaters installed 100-250 m from the shoreline have successfully reclaimed the coastline with a sedimentation rate of 17-23 cm/y. Meanwhile, sand-sausage-submerged breakwaters were ineffective at stabilizing the coastline during 2002-2010 due to land subsidence. With a low subsidence rate, the rubble-mound-submerged breakwaters can reduce the shoreline retreat rate with a vertical deposition rate of about 5 cm/y. In contrast, use of a bamboo fence, a green solution widely used along muddy coasts, traps sediment at a rate of less than 1.3 cm/y and typically lasts only for 2-3 years after installation. Decomposed bamboo causes environmental degradation so local communities disapprove of the approach. Results reveal that grey solutions are more effective for stabilizing the ECPD coastline and result in less coastal environmental impact than the nature-based solution using a bamboo fence.