A5036778229- Air Quality Monitoring and Forecasting
- Air Quality and Health Impacts
- Atmospheric chemistry and aerosols
- Vehicle emissions and performance
- COVID-19 impact on air quality
- Urban Transport and Accessibility
- Energy and Environment Impacts
- Semantic Web and Ontologies
- Conferences and Exhibitions Management
- Human Mobility and Location-Based Analysis
- Forecasting Techniques and Applications
- Atmospheric Ozone and Climate
- Atmospheric and Environmental Gas Dynamics
- Water Quality Monitoring Technologies
University of Liverpool
2023-2024
Bharati Vidyapeeth Deemed University
2023
Sami Labs (India)
2022
BackgroundRelatively clean cooking fuels such as liquefied petroleum gas (LPG) emit less fine particulate matter (PM2·5) and carbon monoxide (CO) than polluting (eg, wood, charcoal). Yet, some interventions have not achieved substantial exposure reductions. This study evaluates determinants of between-community variability in exposures to household air pollution (HAP) across sub-Saharan Africa.MethodsIn this measurement study, we recruited households primarily with LPG or exclusively wood...
The Tamil Nadu Air Pollution and Health Effects study (TAPHE-2) aims to evaluate the relationship between air pollution birth outcome in a rural-urban cohort of 300 pregnant women. Due COVID-19 related lockdowns, some TAPHE-2 activities were delayed; however, continuous indoor outdoor quality data collected around Chennai, India. We report here impact graded lockdown on particulate matter (PM2.5 PM10) levels based calibrated from affordable real-time PM sensors called atmos™ ambient publicly...
Abstract. Lower-cost air pollution sensors can fill critical quality data gaps in India, which experiences very high fine particulate matter (PM2.5) but has sparse regulatory monitoring. Challenges for low-cost PM2.5 India include high-aerosol mass concentrations and pronounced regional seasonal gradients aerosol composition. Here, we report on a detailed long-time performance evaluation of popular sensor, the Purple Air PA-II, at multiple sites India. We established three distinct across...
Low-cost sensors (LCSs) have revolutionized the air pollution monitoring landscape. However, sensitivities of particulate matter (PM) LCS measurements to various particle microphysical properties and meteorological aspects warrant an accuracy investigation. We investigated inter- intracity variations in LCS-measured PM2.5 across geographically demographically distinct Indian cities. The collocation data (collected during March–April 2022) from (Atmos) a reference-grade instrument (β...
Abstract. We report on the long-term performance of a popular low-cost PM2.5 sensor, PurpleAir PA-II, at multiple sites in India, with aim identifying robust calibration protocols. established 3 distinct India (North India: Delhi, Hamirpur; South Bangalore), where we collocated PA-II reference beta-attenuation monitors to characterize sensor and model relationships between PA-IIs for hourly data. Our remained operation across all major seasons India. Without calibration, had high precision...
Stationary air-quality monitors do not capture spatial variations in air-pollution.Mobilemonitoring or "sensors on a mobile platform", is an increasingly popular approach to measure high-resolution pollution data at the street level.Coupled with location data, visualisation of parameters helps detect localized areas high air-pollution, also called hotspots.In this approach, portable sensors are mounted vehicle and driven predetermined routes collect frequency (1 Hz).Analysing involves...
Mobile monitoring can supplement regulatory measurements, particularly in low-income countries where stationary monitors’ density is low. Here, we report results from a ~year-long mobile campaign of on-road black carbon (BC) mass concentration, ultrafine particle (UFP) number and dioxide (CO2) Bengaluru, India. The study route included ~150 unique kms covering urban peri-urban residential neighborhoods the central business district (CBD); ~22 repeat measurements were made per monitored...
Mobile monitoring provides robust measurements of air pollution. However, resource constraints often limit the number so that assessments cannot be obtained in all locations interest. In response, surrogate measurement methodologies, such as videos and images, have been suggested. Previous studies pollution images used static (e.g., satellite or Google Street View images). The current study was designed to develop deep learning methodologies infer on-road pollutant concentrations from...
OPS 46: Exposure assessment to air pollution in Asia and Africa, Room 315, Floor 3, August 27, 2019, 4:30 PM - 5:30 INTRODUCTION Mobile monitoring campaigns that repeatedly cover all roads an area ("wall-to-wall driving") have shown they can robustly capture spatial patterns on-roadway pollution. Yet, this technique has not been tried a low-income country. India is home several of the world's most polluted cities. We explore potential for mobile provide high-resolution exposure data Indian...
Air pollution impacts human health, quality of living, climate, and the economy (Hystad et
High-resolution spatial maps of air pollution can be useful for quality management. In low- and middle-income countries, regulatory measurements criteria pollutants are typically insufficient to generate maps, due the sparsely located monitoring stations. Alternatively, high-resolution achieved by dispersion (physics-based) statistical regression (training-based) modelling. Resolutions up ~25 meters Land Use Regression (LUR) modelling based predictions. this study, we leveraged high density...
INTRODUCTION--Mobile monitoring can effectively capture spatial patterns in on-road exposure. Here, we report measured concentrations of black carbon and ultrafine particles urban peri-urban Bangalore, India.METHODS--Our mobile platform, a CNG car, was equipped with an aethalometer for (BC) condensation particle counter (UFP). We sampled on roadways four parts Bangalore: central business district, residential neighborhood, along urban-rural transect; total, collected ~400 h (~5000 km) data....