Analysis and interpretation of particulate matter – PM10, PM2.5 and PM1 emissions from the heterogeneous traffic near an urban roadway

AbstractThis paper presents analysis and interpretation of diurnal, weekly and seasonal cycles of 1–h average particulate matter (PM10, PM2.5 and PM1) concentrations measured near an urban roadway in Chennai city, India, between November 2007 and May 2008. The PM data analysis showed clear diurnal, weekly and seasonal cycles at the study site. In diurnal cycle, highest PM concentrations were observed during weekday’s peak hour traffic and lowest PM concentrations were found during trickle traffic (afternoon and nighttime). The seasonal PM data analysis showed highest concentrations during post monsoon season (PM10 = 189, PM2.5 = 84, PM1 = 66μg/m3) compared to winter (PM10 = 135, PM2.5 = 73, PM1 = 59μg/m3) and summer (PM10 = 102, PM2.5 = 50, PM1 = 34μg/m3) seasons. The particle size distribution during post-monsoon, winter and summer seasons showed two distinct modes viz. accumulation (mean diameter, d = 2.2μm; distribution = 40%) and coarse (d = 7.1μm, distribution = 60%).The frequency distribution of PM10 concentrations during post–monsoon and winter seasons indicated that the PM10 values at the study site fall under moderate to poor categories. During post–monsoon and winter seasons, it was found that more than 50% of the time the 24–h average PM10 concentrations were violating the Indian national ambient air quality standards (NAAQS) (100μg/m3) and world health organization (WHO) standard (50μg/m3). The 24–h average PM2.5 concentrations were also exceeding the NAAQS (60μg/m3) and WHO standards (25μg/m3) by 75% of time, irrespective of seasons

Seasonal Analysis of Particulate Matter Concentrations at a Heavily Trafficked Urban Site

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Prediction of Indoor Air Quality in a School Building Using Risk Model

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Urban local air quality management framework for non-attainment areas in Indian cities

© 2017 Elsevier B.V. Increasing urban air pollution level in Indian cities is one of the major concerns for policy makers due to its impact on public health. The growth in population and increase in associated motorised road transport demand is one of the major causes of increasing air pollution in most urban areas along with other sources e.g., road dust, construction dust, biomass burning etc. The present study documents the development of an urban local air quality management (ULAQM) framework at urban hotspots (non-attainment area) and a pathway for the flow of information from goal setting to policy making. The ULAQM also includes assessment and management of air pollution episodic conditions at these hotspots, which currently available city/regional-scale air quality management plans do not address. The prediction of extreme pollutant concentrations using a hybrid model differentiates the ULAQM from other existing air quality management plans. The developed ULAQM framework has been applied and validated at one of the busiest traffic intersections in Delhi and Chennai cities. Various scenarios have been tested targeting the effective reductions in elevated levels of NOx and PM2.5 concentrations. The results indicate that a developed ULAQM framework is capable of providing an evidence-based graded action to reduce ambient pollution levels within the specified standard level at pre-identified locations. The ULAQM framework methodology is generalised and therefore can be applied to other non-attainment areas of the country

PM2.5-related health and economic loss assessment for 338 Chinese cities

China is in a critical stage of ambient air quality management after global attention on pollution in its cities. Industrial development and urbanization have led to alarming levels of air pollution with serious health hazards in densely populated cities. The quantification of cause-specific PM2.5-related health impacts and corresponding economic loss estimation is crucial for control policies on ambient PM2.5 levels. Based on ground-level direct measurements of PM2.5 concentrations in 338 Chinese cities for the year 2016, this study estimates cause-specific mortality using integrated exposure-response (IER) model, non-linear power law (NLP) model and log-linear (LL) model followed by morbidity assessment using log-linear model. The willingness to pay (WTP) and cost of illness (COI) methods have been used for PM2.5-attributed economic loss assessment. In 2016 in China, the annual PM2.5 concentration ranged between 10 and 157 μg/m3 and 78.79% of the total population was exposed to >35 μg/m3 PM2.5 concentration. Subsequently, the national PM2.5-attributable mortality was 0.964 (95% CI: 0.447, 1.355) million (LL: 1.258 million and NPL: 0.770 million), about 9.98% of total reported deaths in China. Additionally, the total respiratory disease and cardiovascular disease-specific hospital admission morbidity were 0.605 million and 0.364 million. Estimated chronic bronchitis, asthma and emergency hospital admission morbidity were 0.986, 1.0 and 0.117 million respectively. Simultaneously, the PM2.5 exposure caused the economic loss of 101.39 billion US$, which is 0.91% of the national GDP in 2016. This study, for the first time, highlights the discrepancies associated with the three commonly used methodologies applied for cause-specific mortality assessment. Mortality and morbidity results of this study would provide a measurable assessment of 338 cities to the provincial and national policymakers of China for intensifying their efforts on air quality improvement

Comparative Evaluation of Air Quality Dispersion Models for Predicting Particulate Matter Concentrations at a Busy Traffic Roadway in Chennai City

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Sens-BERT: Enabling Transferability and Re-calibration of Calibration Models for Low-cost Sensors under Reference Measurements Scarcity

Low-cost sensors measurements are noisy, which limits large-scale adaptability in airquality monitoirng. Calibration is generally used to get good estimates of air quality measurements out from LCS. In order to do this, LCS sensors are typically co-located with reference stations for some duration. A calibration model is then developed to transfer the LCS sensor measurements to the reference station measurements. Existing works implement the calibration of LCS as an optimization problem in which a model is trained with the data obtained from real-time deployments; later, the trained model is employed to estimate the air quality measurements of that location. However, this approach is sensor-specific and location-specific and needs frequent re-calibration. The re-calibration also needs massive data like initial calibration, which is a cumbersome process in practical scenarios. To overcome these limitations, in this work, we propose Sens-BERT, a BERT-inspired learning approach to calibrate LCS, and it achieves the calibration in two phases: self-supervised pre-training and supervised fine-tuning. In the pre-training phase, we train Sens-BERT with only LCS data (without reference station observations) to learn the data distributional features and produce corresponding embeddings. We then use the Sens-BERT embeddings to learn a calibration model in the fine-tuning phase. Our proposed approach has many advantages over the previous works. Since the Sens-BERT learns the behaviour of the LCS, it can be transferable to any sensor of the same sensing principle without explicitly training on that sensor. It requires only LCS measurements in pre-training to learn the characters of LCS, thus enabling calibration even with a tiny amount of paired data in fine-tuning. We have exhaustively tested our approach with the Community Air Sensor Network (CAIRSENSE) data set, an open repository for LCS.Comment: 1

Covid-19 impact on air quality in megacities

Air pollution is among the highest contributors to mortality worldwide, especially in urban areas. During spring 2020, many countries enacted social distancing measures in order to slow down the ongoing Covid-19 pandemic. A particularly drastic measure, the "lockdown", urged people to stay at home and thereby prevent new Covid-19 infections. In turn, it also reduced traffic and industrial activities. But how much did these lockdown measures improve air quality in large cities, and are there differences in how air quality was affected? Here, we analyse data from two megacities: London as an example for Europe and Delhi as an example for Asia. We consider data during and before the lockdown and compare these to a similar time period from 2019. Overall, we find a reduction in almost all air pollutants with intriguing differences between the two cities. In London, despite smaller average concentrations, we still observe high-pollutant states and an increased tendency towards extreme events (a higher kurtosis during lockdown). For Delhi, we observe a much stronger decrease of pollution concentrations, including high pollution states. These results could help to design rules to improve long-term air quality in megacities.Comment: 13 pages. Preliminary version of Supplementary Information and open code available here https://osf.io/jfw7n/?view_only=9b1d2320cf2c46a1ad890dff079a2f6

Performance Evaluation of UK ADMS-Urban Model and AERMOD Model to Predict the PM10 Concentration for Different Scenarios at Urban Roads in Chennai, India and Newcastle City, UK

The pollutants and its effects on human health are now a major issue around the world. The impact of traffic and the resulting vehicle emissions has come to the forefront. Particulate matter is one among six criteria pollutants and air pollution related to particulate matter is now becoming a serious problem in developing as well as developed countries. One of the main sources is from the vehicles and the resuspension caused by the vehicular movement. Source apportionment studies of Chennai (Clean Air Asia: Air quality profile 2010 edition) showed that from the residential monitoring stations levels of particulate matter in Chennai lies in the range of 51–70 µg/m3. According to DoT of the total road emissions in UK, about 80 is generated from particulate matter which is due to road traffic even though there are no factors like resuspension in this country. In UK, 103 areas have been declared as local air quality management areas (LAQMA), while in India, 72 cities have been identified as non-attainment area with respect to various air pollutants. Chennai, India and Newcastle City, UK which are the cities under study are the one among them facing severe air pollution problems. The main objective of the paper is application and evaluation of UK ADMS-Urban and AERMOD model for the prediction of particulate matter (PM10) concentrations at urban roadways in Chennai and in Newcastle. The model evaluation has been carried out using traffic data of 2009, meteorological data provided by Laga Systems, Hyderabad for both the cities and the real-time monitored data of the year 2009. The results of the study identified the trends in pollutant patterns and its variation with the different parameters of meteorological data. The statistical descriptors, namely index of agreement (IA), fractional bias (FB), normalized mean square error (NMSE), geometric mean bias (MG) and geometric mean variance (VG) were used to understand the performance of the model. Results indicated that both the models have been able to predict the pollutant concentration with reasonable accuracy. The IA values for ADMS and AERMOD are found to be 0.39 and 0.37, and 0.48 and 0.44, respectively, for Chennai and Newcastle City

Context-based analysis of urban air quality using an opportunistic mobile sensor network

C