Impact of SAFAR Air Quality Forecasting Framework and Advisory Services in Reducing the Economic Health Burden of India

2021 ◽  
pp. 211-225
Author(s):  
Suvarna Tikle ◽  
Tanmay Ilame ◽  
Gufran Beig
Keyword(s):  
Air Pollution ◽  
Allergic Rhinitis ◽  
Air Quality ◽  
Treatment Cost ◽  
Weather Forecasting ◽  
Health Sector ◽  
Economic Loss ◽  
Economic Benefits ◽  
Mitigation Measures ◽  
Annual Treatment

The economic loss attributable to air pollution and associated disease burden is increasing in polluted megacities all over the globe; Indian megacities are no exception. India has launched the System of Air Quality and Weather Forecasting and Research (SAFAR) framework to provide air pollution health advisories well in advance through various outreach activities. We hereby estimate the economic benefit of SAFAR outreach attributed to prevention by intervention through an early warning based on a probabilistic scenario adopted in this work for the top two megacities of India, namely, Delhi and Pune, for the period 2011-2012 to 2019-2020 and 2014-2015 to 2019-2020 respectively. This study considers the cost-saving in pulmonary (Asthma, COPD, etc.) and other related diseases linked to air pollution. Results show that the annual average total cost of all diseases in Pune and Delhi is INR 9,480 million and INR 76,940 million respectively. We found that the total annual treatment cost of Allergic rhinitis OPD treatment cost was the highest (INR 14,490 Million) followed by asthma (INR 10,010 Million), and COPD (INR 5,140 Million) in Delhi during the year 2012. In Pune, annual treatment costs of Allergic Rhinitis, COPD and Asthma were INR 3,590, 890 and 710 Million respectively during the year 2015. SAFAR framework can make average annual savings of ≃INR 10,960 million in Delhi and ≃INR 1,000 million in Pune in the health sector, even if only 5% of the total affected sick population takes advantage of its services. Looking at the huge economic benefits, it is envisaged that the SAFAR framework model may be replicated in many more cities along with other mitigation measures rigorously.

scholarly journals EURODELTA-Trends, a multi-model experiment of air quality hindcast in Europe over 1990–2010

2017 ◽  
Vol 10 (9) ◽  
pp. 3255-3276 ◽  
Author(s):  
Augustin Colette ◽  
Camilla Andersson ◽  
Astrid Manders ◽  
Kathleen Mar ◽  
Mihaela Mircea ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Boundary Conditions ◽  
Radiative Forcing ◽  
Regional Scale ◽  
Air Pollutant ◽  
Pollutant Emissions ◽  
Mitigation Measures ◽  
Modelling Experiment ◽  
Technical Features

Abstract. The EURODELTA-Trends multi-model chemistry-transport experiment has been designed to facilitate a better understanding of the evolution of air pollution and its drivers for the period 1990–2010 in Europe. The main objective of the experiment is to assess the efficiency of air pollutant emissions mitigation measures in improving regional-scale air quality. The present paper formulates the main scientific questions and policy issues being addressed by the EURODELTA-Trends modelling experiment with an emphasis on how the design and technical features of the modelling experiment answer these questions. The experiment is designed in three tiers, with increasing degrees of computational demand in order to facilitate the participation of as many modelling teams as possible. The basic experiment consists of simulations for the years 1990, 2000, and 2010. Sensitivity analysis for the same three years using various combinations of (i) anthropogenic emissions, (ii) chemical boundary conditions, and (iii) meteorology complements it. The most demanding tier consists of two complete time series from 1990 to 2010, simulated using either time-varying emissions for corresponding years or constant emissions. Eight chemistry-transport models have contributed with calculation results to at least one experiment tier, and five models have – to date – completed the full set of simulations (and 21-year trend calculations have been performed by four models). The modelling results are publicly available for further use by the scientific community. The main expected outcomes are (i) an evaluation of the models' performances for the three reference years, (ii) an evaluation of the skill of the models in capturing observed air pollution trends for the 1990–2010 time period, (iii) attribution analyses of the respective role of driving factors (e.g. emissions, boundary conditions, meteorology), (iv) a dataset based on a multi-model approach, to provide more robust model results for use in impact studies related to human health, ecosystem, and radiative forcing.

scholarly journals Understanding Public Response to Air Quality Using Tweet Analysis

2019 ◽  
Vol 5 (3) ◽  
pp. 205630511986765
Author(s):  
Supraja Gurajala ◽  
Suresh Dhaniyala ◽  
Jeanna N. Matthews
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Machine Learning Algorithms ◽  
Mitigation Measures ◽  
Quality Data ◽  
Public Health Issue ◽  
New Delhi ◽  
Public Response ◽  
Pollution Levels ◽  
Adverse Health Effects

Poor air quality is recognized as a major risk factor for human health globally. Critical to addressing this important public-health issue is the effective dissemination of air quality data, information about adverse health effects, and the necessary mitigation measures. However, recent studies have shown that even when public get data on air quality and understand its importance, people do not necessarily take actions to protect their health or exhibit pro-environmental behaviors to address the problem. Most existing studies on public attitude and response to air quality are based on offline studies, with a limited number of survey participants and over a limited number of geographical locations. For a larger survey size and a wider set of locations, we collected Twitter data for a period of nearly 2 years and analyzed these data for three major cities: Paris, London, and New Delhi. We identify the three hashtags in each city that best correlate the frequency of tweets with local air quality. Using tweets with these hashtags, we determined that people’s response to air quality across all three cities was nearly identical when considering relative changes in air pollution. Using machine-learning algorithms, we determined that health concerns dominated public response when air quality degraded, with the strongest increase in concern being in New Delhi, where pollution levels are the highest among the three cities studied. The public call for political solutions when air quality worsens is consistent with similar findings with offline surveys in other cities. We also conducted an unsupervised learning analysis to extract topics from tweets in Delhi and studied their evolution over time and with changing air quality. Our analysis helped extract relevant words or features associated with different air quality–related topics such as air pollution policy and health. Also, the topic modeling analysis revealed niche topics associated with sporadic air quality events, such as fireworks during festivals and the air quality impact on an outdoor sport event. Our approach shows that a tweet-based analysis can enable social scientists to probe and survey public response to events such as air quality in a timely fashion and help policy makers respond appropriately.

scholarly journals Application of an Adaptive Nudging Scheme in Air Quality Forecasting in China

2008 ◽  
Vol 47 (8) ◽  
pp. 2105-2114 ◽  
Author(s):  
Xiangde Xu ◽  
Lian Xie ◽  
Xinghong Cheng ◽  
Jianming Xu ◽  
Xiuji Zhou ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Weather Forecasting ◽  
Initial Conditions ◽  
Primary Source ◽  
Emission Data ◽  
Weather Observations ◽  
Pollution Emissions ◽  
Air Pollution Emissions ◽  
Source Of Error

Abstract A major challenge for air quality forecasters is to reduce the uncertainty of air pollution emission inventory. Error in the emission data is a primary source of error in air quality forecasts, much like the effect of error in the initial conditions on the accuracy of weather forecasting. Data assimilation has been widely used to improve weather forecasting by correcting the initial conditions with weather observations. In a similar way, observed concentrations of air pollutants can be used to correct the errors in the emission data. In this study, a new method is developed for estimating air pollution emissions based on a Newtonian relaxation and nudging technique. Case studies for the period of 1–25 August 2006 in 47 cities in China indicate that the nudging technique resulted in improved estimations of sulfur dioxide (SO2) and nitrogen dioxide (NO2) emissions in the majority of these cities. Predictions of SO2 and NO2 concentrations in January, April, August, and October using the emission estimations derived from the nudging technique showed remarkable improvements over those based on the original emission data.

scholarly journals Spatio-Temporal Variations of Multiple Primary Air Pollutants Emissions in Beijing of China, 2006–2015

Atmosphere ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 494 ◽  
Author(s):  
Yifeng Xue ◽  
Shihao Zhang ◽  
Zhen Zhou ◽  
Kun Wang ◽  
Kaiyun Liu ◽  
...  
Keyword(s):  
Air Pollution ◽  
Quality Improvement ◽  
Air Quality ◽  
Air Pollutants ◽  
Clean Energy ◽  
Mitigation Measures ◽  
Source Control ◽  
Energy Structure ◽  
Pollution Mitigation ◽  
Air Quality Improvement

Air pollution in Beijing, China has attracted continuous worldwide public attention along with the rapid urbanization of the city. By implementing a set of air pollution mitigation measures, the air quality of Beijing has been gradually improved in recent years. In this study, the intrinsic factors leading to air quality improvement in Beijing are studied via a quantitative evaluation of the temporal and spatial changes in emissions of primary air pollutants over the past ten years. Based on detailed activity levels of each economic sector and a localized database containing source and pollutant specific emission factors, an integrated emissions inventory of primary air pollutants discharged from various sources between 2006 and 2015 is established. With the implementation of phased air pollution mitigation measures, and the Clean Air Action Plan, the original coal-dominated energy structure in Beijing has undergone tremendous changes, resulting in the substantial reduction of multiple air pollutants. The total of emissions of six major atmospheric pollutants (PM10, PM2.5, SO2, NOX, VOCs and NH3) in Beijing decreased by 35% in 2015 compared to 2006—this noticeable decrease was well consistent with the declining trend of ambient concentration of criterion air pollutants (SO2, PM10, PM2.5 and NO2) and air quality improvement, thus showing a good correlation between the emission of air pollutants and the outcome of air quality. SO2 emission declined the most, at about 71.7%, which was related to the vigorous promotion of combustion source control, such as the shutdown of coal-fired facilities and domestic stoves and transition to clean energy, like natural gas or electricity. Emissions of PM decreased considerably (by 48%) due to energy structure optimization, industrial structure adjustments, and end-of-pipe PM source control. In general, NOX, NH3, and VOCs decreased relatively slightly, by 25%, 14%, and 2%, respectively, and accordingly, they represented the limiting factors for improving air quality and the key points of air pollution mitigation in Beijing for the future.

scholarly journals Comparison of emissions inventories of anthropogenic air pollutants and greenhouse gases in China

2017 ◽  
Vol 17 (10) ◽  
pp. 6393-6421 ◽  
Author(s):  
Eri Saikawa ◽  
Hankyul Kim ◽  
Min Zhong ◽  
Alexander Avramov ◽  
Yu Zhao ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Emission Inventory ◽  
Air Pollutant ◽  
Central China ◽  
Pollutant Emissions ◽  
Mitigation Measures ◽  
Economic Activities ◽  
Temporal And Spatial Distribution ◽  
Emissions Inventories

Abstract. Anthropogenic air pollutant emissions have been increasing rapidly in China, leading to worsening air quality. Modelers use emissions inventories to represent the temporal and spatial distribution of these emissions needed to estimate their impacts on regional and global air quality. However, large uncertainties exist in emissions estimates. Thus, assessing differences in these inventories is essential for the better understanding of air pollution over China. We compare five different emissions inventories estimating emissions of carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOx), sulfur dioxide (SO2), and particulate matter with an aerodynamic diameter of 10 µm or less (PM10) from China. The emissions inventories analyzed in this paper include the Regional Emission inventory in ASia v2.1 (REAS), the Multi-resolution Emission Inventory for China (MEIC), the Emission Database for Global Atmospheric Research v4.2 (EDGAR), the inventory by Yu Zhao (ZHAO), and the Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS). We focus on the period between 2000 and 2008, during which Chinese economic activities more than doubled. In addition to national totals, we also analyzed emissions from four source sectors (industry, transport, power, and residential) and within seven regions in China (East, North, Northeast, Central, Southwest, Northwest, and South) and found that large disagreements exist among the five inventories at disaggregated levels. These disagreements lead to differences of 67 µg m−3, 15 ppbv, and 470 ppbv for monthly mean PM10, O3, and CO, respectively, in modeled regional concentrations in China. We also find that all the inventory emissions estimates create a volatile organic compound (VOC)-limited environment and MEIC emissions lead to much lower O3 mixing ratio in East and Central China compared to the simulations using REAS and EDGAR estimates, due to their low VOC emissions. Our results illustrate that a better understanding of Chinese emissions at more disaggregated levels is essential for finding effective mitigation measures for reducing national and regional air pollution in China.

scholarly journals Evaluation of multivariate time series clustering for imputation of air pollution data

2021 ◽  
Vol 10 (2) ◽  
pp. 265-285
Author(s):  
Wedad Alahamade ◽  
Iain Lake ◽  
Claire E. Reeves ◽  
Beatriz De La Iglesia
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Air Quality ◽  
Missing Data ◽  
Multivariate Time Series ◽  
Real Data ◽  
Economic Benefits ◽  
Air Pollutant ◽  
Monitoring Site ◽  
Concentration Data

Abstract. Air pollution is one of the world's leading risk factors for death, with 6.5 million deaths per year worldwide attributed to air-pollution-related diseases. Understanding the behaviour of certain pollutants through air quality assessment can produce improvements in air quality management that will translate to health and economic benefits. However, problems with missing data and uncertainty hinder that assessment. We are motivated by the need to enhance the air pollution data available. We focus on the problem of missing air pollutant concentration data either because a limited set of pollutants is measured at a monitoring site or because an instrument is not operating, so a particular pollutant is not measured for a period of time. In our previous work, we have proposed models which can impute a whole missing time series to enhance air quality monitoring. Some of these models are based on a multivariate time series (MVTS) clustering method. Here, we apply our method to real data and show how different graphical and statistical model evaluation functions enable us to select the imputation model that produces the most plausible imputations. We then compare the Daily Air Quality Index (DAQI) values obtained after imputation with observed values incorporating missing data. Our results show that using an ensemble model that aggregates the spatial similarity obtained by the geographical correlation between monitoring stations and the fused temporal similarity between pollutant concentrations produces very good imputation results. Furthermore, the analysis enhances understanding of the different pollutant behaviours and of the characteristics of different stations according to their environmental type.

scholarly journals Forecasting the air pollution episode potential in the Canary Islands

2008 ◽  
Vol 2 (1) ◽  
pp. 21-26 ◽  
Author(s):  
C. Milford ◽  
C. Marrero ◽  
C. Martin ◽  
J. J. Bustos ◽  
X. Querol
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Canary Islands ◽  
Weather Forecasting ◽  
Local Area ◽  
Quality Data ◽  
Boundary Layer Height ◽  
Pollution Episode ◽  
Synoptic Wind ◽  
Environment Programme

Abstract. In the frame of the WMO Global Atmosphere Watch Urban Research Meteorology and Environment programme (GURME), a system for forecasting air pollution episode potential in the Canary Islands has been developed. Meteorological parameters relevant to air quality (synoptic wind speed, wind direction, boundary layer height and temperature at 91 vertical levels) are obtained from the European Centre for Medium range Weather Forecasting (ECMWF) once a day for up to four days ahead. In addition, a model based on the analogue method utilising six years of historical meteorological and air quality data predicts the probability of SO2 concentration exceeding certain thresholds for a measurement station located in Santa Cruz de Tenerife. Meteorological forecasts are also provided from a high resolution (2 km) local area model (MM5) implemented for the Canary Islands domain. This simple system is able to forecast meteorological conditions which are favourable to the occurrence of pollution episodes for the forthcoming days.

scholarly journals EURODELTA-Trends, a multi-model experiment of air quality hindcast in Europe over 1990–2010

2017 ◽  
Author(s):  
Augustin Colette ◽  
Camilla Andersson ◽  
Astrid Manders ◽  
Kathleen Mar ◽  
Mihaela Mircea ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Boundary Conditions ◽  
Radiative Forcing ◽  
Regional Scale ◽  
Air Pollutant ◽  
Pollutant Emissions ◽  
Mitigation Measures ◽  
Modelling Experiment ◽  
Technical Features

Abstract. The Eurodelta-Trends multi-model chemistry-transport experiment has been designed to facilitate a better understanding of the evolution of air pollution and its drivers for the period 1990–2010 in Europe. The main objective of the experiment is to assess the efficiency of air pollutant emissions mitigation measures in improving regional scale air quality. The present paper formulates the main scientific questions and policy issues being addressed by the Eurodelta-Trends modelling experiment with an emphasis on how the design and technical features of the modelling experiment answer these questions. The experiment is designed in three tiers with increasing degree of computational demand in order to facilitate the participation of as many modelling teams as possible. The basic experiment consists of simulations for the years 1990, 2000 and 2010. Sensitivity analysis for the same three years using various combinations of (i) anthropogenic emissions, (ii) chemical boundary conditions and (iii) meteorology complements it. The most demanding tier consists two complete time series from 1990 to 2010, simulated using either time varying emissions for corresponding years or constant emissions. Eight chemistry-transport models have contributed with calculation results to at least one experiment tier, and three models have – to date – completed the full set of simulations (and 21-year trend calculations have been performed by four models). The modelling results are publicly available for further use by the scientific community. The main expected outcomes are (i) an evaluation of the models performances for the three reference years, (ii) an evaluation of the skill of the models in capturing observed air pollution trends for the 1990–2010 time period, (iii) attribution analyses of the respective role of driving factors (emissions/boundary conditions/meteorology), (iv) a dataset based on a multi-model approach, to provide more robust model results for use in impact studies related to human health, ecosystem and radiative forcing.

scholarly journals Escaping from air pollution: The psychological process of domestic migration intention among urban people

2021 ◽  
Author(s):  
Quan-Hoang Vuong ◽  
Tam-Tri Le ◽  
Nguyen Quang-Loc ◽  
Trung Quang Nguyen ◽  
Minh-Hoang Nguyen
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
City Planning ◽  
Sustainable Urban Development ◽  
Mitigation Measures ◽  
Psychological Process ◽  
Rapid Urbanization ◽  
Migration Intention ◽  
Perceived Availability ◽  
Urban People

Rapid urbanization with poor city planning has resulted in severe air pollution in low- and middle-income countries’ urban areas. Given the adverse impacts of air pollution, many responses have been taken, including migration to another city. The current study explores the psychological process and demographic predictors of migration intention among urban people in Hanoi, Vietnam – one of the most polluted capital cities in the world. The Bayesian Mindsponge Framework (BMF) was used to construct the model and perform Bayesian analysis on a stratified random sampling dataset of 475 urban people. We found that the migration intention was negatively associated with the individual’s satisfaction with air quality. The association was moderated by the perceived availability of a better alternative (or nearby city with better air quality). However, the high migration cost due to geographical distance affected the perceived availability of a better alternative negligible. Moreover, it was also found that male and young people were more likely to migrate, but the brain drain hypothesis was not validated. The results hint that without air pollution mitigation measures, the dislocation of economic forces might occur and hinder sustainable urban development. Therefore, collaborative actions among levels of government, with the semi-conducting principle at heart, are recommended to reduce air pollution.

scholarly journals Are children who are treated for asthma and seasonal allergic rhinitis disadvantaged in their educational attainment when acutely exposed to air pollution and pollen? A feasibility study

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Amy Mizen ◽  
Jane Lyons ◽  
Sarah Rodgers ◽  
Damon Berridge ◽  
Ashley Akbari ◽  
...  
Keyword(s):  
Air Pollution ◽  
Allergic Rhinitis ◽  
Health Status ◽  
Air Quality ◽  
Educational Attainment ◽  
Linear Regression Analysis ◽  
Treatment Seeking ◽  
Exam Performance ◽  
Negatively Associated ◽  
Individual Level

BackgroundThere is a lack of evidence of the adverse effects which air quality has on cognition for people with air quality-related health conditions, these are not widely documented in the literature. Educational attainment, as a proxy for cognition, may increase with improved air quality. ObjectivesPrepare individual and household level linked environmental and health data for analysis within an anonymised safe haven; analyse the linked dataset for our study investigating: Cognition, Respiratory Tract illness and Effects of eXposure (CORTEX). MethodsAnonymised, routinely collected health and education data were linked with high spatial resolution pollution measurements and daily pollen measurements to provide repeated cross-sectional cohorts (2009-2015) on 18,241 pupils across the city of Cardiff, using the SAIL databank. A fully adjusted multilevel linear regression analysis examined associations between health status and/or air quality. Cohort, school and individual level confounders were controlled for. We hope that using individual-level multi-location daily exposure assessment will help to clarify the role of traffic and prevent potential community-level confounding. Combined effects of air quality on variation in educational attainment between those treated for asthma and/or Severe Allergic Rhinitis (SAR), and those not treated, was also investigated. FindingsAsthma was not associated with exam performance (p=0.7). However, SAR was positively associated with exam performance (p<0.001). Exposure to air pollution was negatively associated with educational attainment regardless of health status. ConclusionsIrrespective of health status, air quality was negatively associated with educational attainment. Treatment seeking behaviour may explain the positive association between SAR and educational attainment. For a more accurate reflection of health status, health outcomes not subject to treatment seeking behaviours, such as emergency hospital admission, should be investigated.

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