Chennai Metropolitan Research Articles

Health risk assessment of gaseous pollutants and particulate matter in an educational institute in the Chennai Metropolitan Region, India

ABSTRACT The prolonged exposure to air pollution increases the risk of major health problems, and hence, this study analyses PM2.5, NO2, SO2 and bioaerosol levels in an educational institution located along a busy state highway (road-side environment) in Chennai, Tamil Nadu, India. The findings demonstrate that the daily average values of gaseous pollutants (26.51 ± 3.08 µg/m3 and 48.15 + 11.30 µg/m3) and PM2.5 (41.04 ± 18.30 µg/m3) were well below the permissible bounds. However, PM2.5 concentration was higher than the safe limit (> 60 µg/m3) over a few days during the sampling period and high probabilities of contributions from the southern and south-western directions. The average respiratory deposition doses obtained for the sit and walk modes were 0.28 and 0.82 μg min−1 respectively and also found that RDD values for males across the alveolar region, tracheobronchial tree, and upper airway was found to be high. Additionally, inhalable bacteria were found in the PM2.5, suggesting that both risk factors have the potential to cause respiratory diseases. These findings provide a basis for policymakers to develop precautionary measures against pollutant exposure and underscore the need for green infrastructure in educational institutes to safeguard the younger generations from developing chronic respiratory diseases.

Identification and quantification of localized urban heat island intensity and footprint for Chennai Metropolitan Area during 1988-2023.

Rapid urbanization has altered land use and land cover to accommodate the growing population. This shift towards urbanization has resulted in the UHI effect, where the inner urban core is notably warmer than its surroundings. Existing research on UHI has primarily focused on major cities at the regional scale, leaving a gap in addressing the effect of extreme UHI zoneswithin a city. This study bridges the gap by developing a methodology to quantify the impact of LULC changeon the localized UHI zones within the urban areas, which will assist policymakers in mitigating urban heat. LULC change matrix analysis and LST retrieval weredone from the Landsat 5 and 8 images acquired between 1988 and 2023. Representative study sites that intersectedwiththe LULCconversion from water bodies and vegetation to other LULC and which showed maximum UHIwere selected. Mean LST was calculated for the proximity of 1000m around the selected areas. The developed methodology was applied to the Chennai Metropolitan Area in Tamil Nadu, India. The conversion of Pallikaranai marshland to the Perungudi dumping ground(PDG), and the green cover to the Kodungaiyur dumping ground(KDG) has led to an average increase in UHI intensity of 0.21°C/year and 0.15°C/year, respectively. The UHI effectis felt at the distance of 450m from PDG and 550m from KDG, which have shown that the life within the proximityare expected to experience the UHI effect. Therefore, it is imperative to alleviate the rising UHI around the selected areas. This developed methodology can be applied globally to select thetargeted UHI zones for sustainable urban planning to mitigate urban heat.

Assessment of Land Use Development Trends along Transport Corridors in Chennai Metropolitan Area

The Chennai Metropolitan Area (CMA) is a rapidly evolving region that experiences significant changes in land use patterns over time. Analogous to other Indian cities, these transformations are particularly evident in the periphery of Chennai's urban core, where substantial land use shifts have occurred between 2001 and 2021. These changes were assessed utilizing LANDSAT 7 Thematic Mapper (TM), LANDSAT 5 Enhanced Thematic Mapper plus (ETM+) for the years 2001 and 2011, and LANDSAT 8 (OLI) for 2021. This study employs unsupervised classification to investigate major land use/land cover alterations. Findings indicate that CMA has undergone substantial land use modifications over the past two decades. Notably, there has been a marked decline in open land and vegetation due to extensive reclamation for urban and commercial development. The results demonstrate that the area covered by open land and vegetation decreased by 70.23% and 4.56%, respectively, while the built-up area increased by 56%. The study also identified a pattern of urban expansion primarily along the GST road, OMR road, and Thirupati highway. The research further examines land use changes by creating buffer zones at distances of 500 meters, 1 km, 1.5 km, and 2 km. The detailed analysis, along with the spatio-temporal differentiation maps and tables generated through geospatial data, will be invaluable for understanding the growth dynamics of CMA. This information will also support the decision-making process for local planners, stakeholders, and academicians.

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.

GIS-based Social Vulnerability Integration for Robust Flood Response, Disease Prevention and Community Resilience in Chennai Metropolitan Area post-Cyclone Michaung

The present study outlines a comprehensive strategy for flood response, disease prevention and long-term community resilience in the aftermath of a recent Cyclone Michaung, with the proximity of landfall near Chennai and the increased likelihood of flooding in the CMA. Drawing insights from an existing social vulnerability map, the study addresses the unique challenges posed by flooding and aims to mitigate the heightened risk of waterborne and vector-borne diseases. Objectives encompass assessing social vulnerability, formulating a targeted flood response strategy, implementing healthcare interventions for disease prevention, strengthening information dissemination and advocating for long-term resilience planning. The methodology involves reviewing the existing vulnerability map, identifying vulnerable communities, integrating flood impact data and developing a comprehensive response plan. The study utilizes GIS and Remote Sensing technologies for monitoring and assessing the results. Key findings from the social vulnerability assessment inform prioritized intervention areas for both flood response and disease prevention. The discussion interprets results in the context of strategy effectiveness, emphasizing the significance of a nuanced and inclusive approach. This manuscript serves as a guide for stakeholders in implementing a holistic strategy that addresses immediate concerns, prevents disease outbreaks and fosters long-term community resilience.

Exploring the Socio-Economic Challenges and Vulnerabilities of Homelessness Individuals in George Town, Chennai

Homelessness is a prevalent social problem in many countries across the globe. India, including Chennai is no exception. The Chennai Metropolitan city, experiencing an influx of residents seeking job opportunities alongside increasing infrastructure and industrial development, has also witnessed a growing number of urban poor who remain homeless. This paper aims to study the multifaceted problems faced by homeless individuals and also to explore the causes leading to homelessness. With the help of detailed surveys, observations, interviews and focused group discussion the basic entitlements, education, health, hygiene and employment aspects are studied at Jaffar Sarang Street and Narayanappa Street of George Town, Chennai.

Impact of Urban Vegetation Loss on Urban Heat Islands: A Case Study of Chennai Metropolitan Area

Impact of Urban Vegetation Loss on Urban Heat Islands: A Case Study of Chennai Metropolitan Area

Environmental Assessment of Toxic Gases and Particulate Matter (PM2.5) Monitoring During Fireworks Episodes (Diwali Festival) in Chennai Metropolitan, Southern India

The Indian celebration of lights, Diwali, is very important. This yearly event in October or November involves spectacular fireworks. Sparklers increase PM and harmful chemicals in the environment. It’s no surprise that events affect air quality. This study measured Total Gaseous Mercury (TGM), Particulate Matter (such as PM2.5), Sulphur Dioxide (SO2), Nitrogen Dioxide (NO2), Nitric Oxide (NO), Carbon Monoxide (CO), Oxides of Nitrogen (NOx), and Ozone (O3) in Chennai, a densely populated metropolitan area in Tamil Nadu, India, before, on, and after Diwali. It was to determine how firecrackers affect air quality. Southwest (SW) and south-southwest winds predominate before Diwali. During Diwali, west-northwest and south-southwest wind speeds were highest. The average wind speed is 0.2-2.0 m/s. The average gaseous component concentration decreased pre- and post-Diwali. Wind direction and gaseous component distribution show Diwali sparklers were the main source of toxicity. A novel method measures wind patterns and velocities before, during and after Diwali to determine air quality dynamics. Gaseous component levels dropped significantly after Diwali, exceeding expectations. Firework emissions, meteorology and pollution dispersion were assessed. Public awareness efforts are needed since sparklers with lighting characteristics are the main source of Diwali pollution, according to the report. This new idea encourages sustainable, health-focused cultural events and urban environmental protection through data-driven decision-making.

ASSESSING THE RELATION BETWEEN LAND USE / LAND COVER CHANGES AND ENVIRONMENTAL PARAMETERS FOR CHENNAI CITY, TAMIL NADU: A REMOTE SENSING AND GIS APPROACH

Abstract. Land use land cover changes in coastal regions are prone to many factors like rapid urbanization and industrialization, tourism, and tideline changes etc. Chennai city, a coastal city of TamilNadu, India has witnessed tremendous changes in land use land cover over the past two decades due to rapid urbanization and industrialization. Remote sensing and GIS technologies can efficiently help in analyzing these changes. In this study, changes in land use land cover patterns of Chennai city from 2005 to 2016 were assessed using IRS P6 LISS III remotely sensed satellite images. Post classification comparison-based change detection method was used to identify the changes in LU/LC. The study shows an increase in compact built-up and industrial areas by 15.36 % and 7.22 % respectively and a decrease in agricultural land, wetlands, and water bodies by 3.66%, 1.73%, and 5.24% respectively over the past decade. The obtained LU/LC changes were correlated with environmental parameters like Land surface temperature (LST), annual average rainfall and average groundwater level to establish a relation. Observations have shown that the temperature in Chennai metropolitan city has increased by 6̊c from 2005 to 2016 and the rainfall has reduced by 100mm from 2005 to 2016. This study demonstrated the relation between LU/LC changes over the past decade and the variations in environmental parameters like increased LST, reduced rainfall and groundwater.

Prediction of Traffic Flow by Sequencing Spatial–Temporal Traffic Dependency on Highways

Traffic flow on highways is a dynamic process in which characteristics of road segments vary in time and space. Traffic congestion is the adverse effect caused dby an increase in travel time which is unprecedented by time and space. Temporal and spatial information of traffic flow is an integral component in the assessment of highway traffic flow. The spatial–temporal traffic flow dependency on highways can be well assessed when temporal traffic information in preceding time instances is sequenced. Thus, the SCAE-LSTM network is proposed considering time and space. This study investigates the estimation of traffic flow on highways based on spatial and temporal traffic sequences. Sequencing highway traffic information has motivated the authors to propose the method. The performance of the method is experimented on State Highways SH 49 and SH 49-A of Chennai Metropolitan City, Tamil Nadu, India. The computational complexity of the method is analyzed empirically. The significant outcome of the proposed method is reported in the experimental study. The traffic flow estimated using the proposed method has shown reduced complexity compared to other baseline methods. Finally, research directions to work in future are presented towards the end.

Impact of air pollutants on climate change and prediction of air quality index using machine learning models

The impact of air pollution in Chennai metropolitan city, a southern Indian coastal city was examined to predict the Air Quality Index (AQI). Regular monitoring and prediction of the Air Quality Index (AQI) are critical for combating air pollution. The current study created machine learning models such as XGBoost, Random Forest, BaggingRegressor, and LGBMRegressor for the prediction of the AQI using the historical data available from 2017 to 2022. According to historical data, the AQI is highest in January, with a mean value of 104.6 g/gm, and the lowest in August, with a mean AQI value of 63.87 g/gm. Particulate matter, gaseous pollutants, and meteorological parameters were used to predict AQI, and the heat map generated showed that of all the parameters, PM2.5 has the greatest impact on AQI, with a value of 0.91. The log transformation method is used to normalize datasets and determine skewness and kurtosis. The XGBoost model demonstrated strong performance, achieving an R2 (correlation coefficient) of 0.9935, a mean absolute error (MAE) of 0.02, a mean square error (MSE) of 0.001, and a root mean square error (RMSE) of 0.04. In comparison, the LightGBM model's prediction was less effective, as it attained an R2 of 0.9748. According to the study, the AQI in Chennai has been increasing over the last two years, and if the same conditions persist, the city's air pollution will worsen in the future. Furthermore, accurate future air quality level predictions can be made using historical data and advanced machine learning algorithms.

Assessing the future groundwater vulnerability of an urban region under variable climatic and land use conditions

Climate change and urbanization threaten groundwater resources due to over-abstraction and contamination. Groundwater vulnerability assessment aids in protecting groundwater resources; DRASTIC is the widely used groundwater vulnerability model (GVM) to assess vulnerability. This study uses an optimized DRASTIC framework to assess the impact of climate and land use change on the future groundwater vulnerability of Chennai Metropolitan Area (CMA). The frequency ratio (FR) and metaheuristic (MH) algorithms are coupled to optimize the rates and weights of GVM to reduce subjectivity. The climatic variables and land use projected for the future period 2030 and 2050 under RCP4.5 and RCP8.5 scenarios. The projected net recharge for the study region varies from 349.95 mm/yr to 458.95 mm/yr, and depth to groundwater level ranges between 4.92 and 6.42 mbgl for scenarios 1 to 4. The RCP4.5 scenario for the far future period (scenario-2) shows an increase in the vulnerability class by 41.64%, and RCP8.5 scenario for the far future period (scenario-4) exhibits a decrease in the vulnerability class by 20.67% of the total area. The integrated groundwater vulnerability analysis found that the CMA groundwater system is in critical condition of high vulnerability for near and far future periods.

IoT- and GIS-Based Environmental Impact Assessment of Construction and Demolition Waste Dump Yards

Globally, construction and demolition wastes (C&DW) are rapidly increasing, occupying 3 billion square yards of land for landfill. In India, C&D debris reaches 150 million tons per year, leading to environmental damage and resource wastage due to poor waste management practices. This study focuses on environmental impact analysis of air and water quality for selected construction and demolition waste dump yards for Chennai metropolitan city in India for two recycling units with 15 legal dumping yards. The Internet of Things (IoT) and Geographic Information System (GIS) is used to monitor and analyze environmental effect due to C&D waste dump yard. The highest concentrations of CO and CO2 in one dump site were observed at one point, with values of 7.49 ppm and 1656.06 ppm, respectively, and at another point with the lowest values of 2.3 ppm and 619.76 ppm. TDS values ranged from 269.2 mg/L to 1347.66 mg/L, and pH values ranged from 6.4 to 11.8, indicating pollution levels in drinking water. The findings suggest that improved waste management practices are urgently needed, including better regulation and enforcement of waste disposal laws, the establishment of recycling facilities with proper air and water pollution control measures, and public education campaigns for responsible waste disposal.

Understanding the relationship between Urban Heat Island and Urban Vegetation by reviewing three decades of satellite images for the Chennai Metropolitan Area

Urbanization has led to one of the most important climatic issues i.e., the Urban Heat Island (UHI) phenomenon. In Chennai Metropolitan Area (CMA), the overall distribution of the green cover has gradually succumbed to urbanization whereas the temperatures have soared by 1.3 deg C in the past six decades. Urban vegetation provides shade and protects the buildings from direct solar exposure, thus reduces the UHI. Further, it also sequester large quantities of carbon, reduce storm water runoff and function as noise filters and pollutant traps. In comparison with the international UHI studies, the studies concerning Indian UHI are limited. Hence, the aim of the current research work is to understand the seasonal relationship between Land Surface Temperature (LST) and the Normalised Difference Vegetation Index (NDVI) of CMA using LANDSAT 4, 5, 7 and 8 images captured during the years 1988, 1991, 1996, 2000, 2008, 2013 and 2016. The results are discussed under three parts namely, Part-A, Part-B, and Part-C. Part-A results infer that the CMA is covered predominantly by minimum LST values (9 deg C to 25 deg C) and maximum LST values (25 deg C to 50 deg C) during Monsoon and Summer/Post-Monsoon seasons, respectively. The mean LST of the CMA is between 18 deg C to 31 deg C throughout the year. In Part B analysis results, the study establishes a moderate seasonal correlation between LST and the NDVI values (r-values between -0.2648 and 0.3604). According to Part C results, ‘High Vegetation’ (NDVI values 0.5 to 1) has a significant role in maintaining the average LST during all four seasons. Further, it also has the potential to reduce the LST values during the summer season in CMA. However, due to a moderate correlation, the urban vegetation may reduce the LST value only as an outcome of reducing the rest of the UHI contributors.

PCA-based approach for mapping social vulnerability to hazards in the Chennai metropolitan area, east coast of India

ABSTRACT Social vulnerability shows the lack of capacities of a person or groups across space and time to prepare for, respond to, and recover from the impact of natural hazards. It involves a combination of socioeconomic and demographic factors that determine the degree to which a (human) system is susceptible to, or unable to cope with, the adverse effects of a disastrous event. Social Vulnerability Index (SoVI) is an effective tool to measure the social vulnerability of an area. Though SoVI has successfully applied in many different contexts and places for socioeconomic development and disaster risk reduction, most societies still lack awareness of how social differences within their population play a role during disastrous events. To address this gap, the present study aims to map the social vulnerability and identify the locations of a socially vulnerable community in the Chennai Metropolitan Area (CMA) through an inductive approach (e.g. factor analysis) using demographic and built-environment data in ArcGIS and SPSS environment. We analysed twenty-three individual variables from five different vulnerability components, such as population, housing, economics, healthcare service, and exposed elements using Principal Component Analysis, that reduced to a smaller set of multidimensional components that explained 71.2% of the total variance and calculated the final SoVI score by adding all five-factor scores. The resultant SoVI map identifies the most vulnerable areas in the highly populated and tightly packed residential areas of Chennai city and the least vulnerable areas on the outskirts of Chennai city. The constructed SoVI could assist planners and policymakers at the national, state, and local government level in making appropriate decisions at all phases of the disaster management cycle and help prioritize the implementation of Government welfare schemes.

Adaptive Traffic Signal Timer for A Signal in Chennai Metropolitan City Using Python and OpenCV

Background: The current traffic control system in India’s metropolises is ineffective because of the randomization of traffic density patterns throughout the day. For a predetermined period of time, the traffic signal timers switch traffic’s direction. Vehicles must therefore wait for a long time even when there is little traffic. Objectives: To continuously adjust the traffic signal timer based on the varying real-time traffic density and to significantly lessen traffic congestion. Methods: An adaptive traffic light timer is offered by this research using image processing algorithms. The real-time image data will be analyzed by the Raspberry Pi and OpenCV, which will determine when to regulate traffic flow. Findings: The adaptive timer minimizes vehicle waiting time and increases the number of turns granted to cars in each lane to cross the signal, which results in an improvement in traffic flow. Novelty: The use of image processing algorithms, the Raspberry Pi, and OpenCV makes the proposed system highly efficient and effective in adapting to the real-time traffic conditions. The suggested methodology will be more affordable than the currently used methodologies. Keywords: Adaptive Traffic Signal Timer; Intelligent Traffic Control

An algorithm to estimate the real time secondary infections in sub-urban bus travel: COVID-19 epidemic experience at Chennai Metropolitan city India.

The online version contains supplementary material available at 10.1007/s13337-022-00804-9.

Dynamics of urban sprawl on the peri-urban landscape and its relationship with urban heat island in Chennai Metropolitan Area, India

Dynamics of urban sprawl on the peri-urban landscape and its relationship with urban heat island in Chennai Metropolitan Area, India

Variations in local, transported, and exposure risks of PM2.5 pollution: Insights from long-term monitoring data in mega coastal city

Urban air quality is influenced by both local and external sources of pollution. The outbreak of Coronavirus COVID-19 had led to implementation of stringent lockdowns at different levels of governance. National Level Lockdown (NLL) was imposed in 2020 and State Level Lockdown (SLL), in 2021 to curb the spread of disease. A direct consequence of lockdown is the dramatic improvement of air quality in various urban agglomerations. The present study investigated the effects of national and regional lockdown on PM2.5 levels at various urban morphological units in the Chennai Metropolitan Region, the fourth-largest urban conglomeration in India. Hourly measurements of PM2.5 over a 6-year period (2016–2021) at three monitoring sites in Chennai were segregated into Pre-lockdown Phase (PLP) (2016–2019), NLL, and SLL to analyze reductions in PM2.5 levels. Local PM2.5 sources were investigated using Conditional Bivariate Probability Function (CBPF) whereas transported PM2.5 was evaluated using Concentration Weighted Trajectory (CWT) and Cluster Analysis. The results showed that PM2.5 in Chennai Metropolitan Region reduced by 33.45% during NLL and 48.95% during SLL when compared to PLP. Mann Kendall Test showed a statistically significant (p < .001) decreasing PM2.5 trend at Manali Monitoring Site, Alandur Monitoring Site, and Velachery Monitoring Site. CBPF showed variations in local contributions for all sites during the selected time frames. CWT and Cluster analysis showed reductions in transported PM2.5 during NLL and SLL when compared to PLP. Health Risk Assessment using Integrated Exposure Response (IER) model function showed a decreasing trend of excess mortality for various health endpoints over 6-year period. Steep reductions in excess mortality cases were observed during NLL and SLL. The results show that lockdown implemented at the state level proved to be more effective than the national level in reducing PM2.5 levels over Chennai metropolitan Region.

Quantify the Behaviour Intention of Individuals to Control SC Performance by Exploring Cloud Storage Services

The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) is explored as a theoretical background to build extended UTAUT2 model with relevant variables for examining the cloud storage services technology acceptance at individual level to respond the current and future SCM operations. The research employed purposive sampling method for data collection. The questionnaire is distributed in booklet format to participants who had experienced in using online cloud storage service platforms (e.g., Google Drive, Microsoft One Drive, etc.). The data are collected from participates dwelling at Chennai metropolitan in South India. The data is analysed by using structural equation modelling technique through Smart PLS 2 software. The performance expectancy, social influence, trust, and perceived speed of access are found to be the strong significant determinants affecting and changing the behavioural intention of individual (customers) towards using cloud storage service technology in managing own firm SCM networks and operations.