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Increased ozone pollution has greatly negative impact on human health. This study aimed to update evidence on the effects of short-term ozone exposure on three major health endpoints in China: all-cause, cardiovascular and respiratory mortality. The specific epidemiological study in a single city or a typical region cannot fully describe the health risk of ozone pollution all over China. Therefore, this study used the results (relative risks) from a meta-analysis based on the epidemiological studies conducted in China for quantitative health burden analysis from 2014 to 2020. In 2013–2017, the implementation of the Air Pollution Prevention and Control Action Plan policy responsible for the increase of ozone concentration during the warm season (i.e., April–September) from 157.9 ± 43.9 μg m−3 to 172.8 ± 32.3 μg m−3. Whereas during the implementation of the Three-Year Action Plan for Blue Sky Defense War (2018–2020) policy, ozone concentration decreased and its value was 157.8 ± 26.2 μg m−3 in 2020. In this study, we found that the premature mortality attributable to short-term exposure to the 4th highest annual daily maximum 8 h ozone concentration in 2020 that was equal to 218.9 (95% CI: 187.3, 255.2), 136.4 (95% CI: 116.5, 154.0) and 24.4 (95% CI: 15.3, 33.3) thousand, increased by 8.73%, 10.40%, and 3.27%, for all-cause, cardiovascular, and respiratory mortality, respectively compared to 2014. At the province-level, Shandong province has the highest number of all-cause mortalities, accounting for 9.42% in 2020, followed by Henan (7.54%), Hebei (7.52%), Guangdong (6.39%) and Jiangsu (6.30%). This study presents a robust assessment based upon a meta-analysis and further provides important insights into new public health research fields. These results of this study provide a scientific reference for optimizing the division of air pollution prevention and control areas and the design of pollutant emission reduction strategies at different time periods.

Access to detailed comparisons of the air quality variations encountered when commuting through a city offers the urban traveller more informed choice on how to minimise personal exposure to inhalable pollutants. In this study we report on an experiment designed to compare atmospheric contaminants, in this case, PM2.5 inhaled during rickshaw, bus, metro, non-air-conditioned car, air-conditioned (AC) car and walking journeys through the city of Delhi, India. The data collection was carried out using a portable TSI SidePak Aerosol Monitor AM520, during February 2018. The results demonstrate that rickshaws (266 ± 159 μg/m3) and walking (259 ± 102 μg/m3) modes were exposed to significantly higher mean PM2.5 levels, whereas AC cars (89 ± 30 μg/m3) and the metro (72 ± 11 μg/m3) had the lowest overall exposure rates. Buses (113 ± 14 μg/m3) and non-AC cars (149 ± 13 μg/m3) had average levels of exposure, but open windows and local factors caused surges in PM2.5 for both transport modes. Closed air-conditioned transport modes were shown to be the best modes for avoiding high concentrations of PM2.5, however other factors (e.g. time of the day, window open or closed in the vehicles) affected exposure levels significantly. Overall, the highest total respiratory deposition doses (RDDs) values were estimated as 84.7 ± 33.4 μg/km, 15.8 ± 9.5 μg/km and 9.7 ± 0.9 μg/km for walking, rickshaw and non-AC car transported mode of journey, respectively. Unless strong pollution control measures are taken, the high exposure to PM2.5 levels will continue causing serious short-term and long-term health concerns for the Delhi residents. Implementing integrated and intelligent transport systems and educating commuters on ways to reduce exposure levels and impacts on commuter's health are required.

Epidemiological studies reported that ozone (O3) is associated with cardiovascular diseases. However, only few of these studies examined the impact of multiple O3 indicators on cardiovascular hospital admissions. This study aimed to explore and compare the impacts of different O3 indicators on cardiovascular hospital admissions in Guangzhou, China. Based upon the data on daily cardiovascular hospital admissions, air pollution, and meteorological factors in Guangzhou from 2014 to 2018, a time-stratified case-crossover design model was used to analyze the associations between different O3 indicators and cardiovascular hospital admissions. Moreover, the sensitivities of different age and gender groups were analyzed for the whole year and different seasons (i.e., warm and cold). During the warm season, for the single-pollutant model, the odds ratio (OR) value of cardiovascular hospital admissions was 1.0067 (95% confidence interval (CI): 1.0037, 1.0098) for every IQR increase in MDA8 O3 at a lag of five days. The effect of O3 on people over 60 year was stronger than that on the 15–60 years age group. Females were more sensitive than males to O3 exposure. These results provided valuable references for further scientific research and environmental improvement in Guangzhou. Given that short-term O3 exposure poses a threat to human health, the government should therefore pay attention to prevention and control policies to reduce and eliminate O3 pollution and protect human health.

In this study, researchers have explored real-world driving conditions and developed emission factors for 58 passenger cars using on-board emission measurement technique while driving on five different routes in Delhi. The measured average emission factors of CO, HC, and NO were 3.99, 0.34, and 0.54 g/km for diesel vehicles, 7.26, 0.17, and 0.62 for petrol vehicles respectively. Road, traffic, vehicle type, and driving characteristics affect the quantity of emissions released. However, speed and acceleration significantly impact emission rates increasing with the increase in speed and acceleration. Also, emissions were minimal at 40–60 kmph and −0.5–0.5 m/s2. The estimated city-wide CO, HC, and NO emissions were 60.8, 4.8, and 9.72tonnes/day. These results demonstrate the importance of monitoring the real-world exhaust emissions given the substantial difference between test cycle measurements used for compliance testing of new vehicles.

The present study is an attempt to apply principal component analysis (PCA) for spatial assessment of water quality parameters that are responsible for water quality deterioration in the Ganga river at four cities of Uttar Pradesh. 48 water samples of the Ganga river from Allahabad, Mirzapur, Shahzadpur and Varanasi in Uttar Pradesh were collected during January to December in 2013. Data were analyzed for assessment of 18 different physicochemical and biological water quality parameters. These variables were examined using PCA to define and standardize the parameters mainly responsible for water quality variance in Ganga river at four selected cities. PCA highlighted anthropogenic effect and industrial effect as two main significant components which explain more than 99.32% of the variance, accounting for 64.47 and 34.85% respectively of the total variance of Ganga water quality at the four cities. Results revealed that total dissolved solids, total alkalinity, total hardness were the important parameters in assessing variations in Ganga water quality during October to April (post-monsoon months) and turbidity, suspended solids are the important parameters in assessing variations in Ganga water quality during June to September (monsoon months). Ca2+, Cl–, $${\text{SO}}_{4}^{{2 - }}$$ , temperature, fluoride, pH, Fe, Cl–, were found to be non-principal water quality parameters. Principal component analysis produced three significant main components explaining more than 82.9% of the variance (anthropogenic and industrial effect) that present 57.1, 13.8 and 12% respectively of the total variance of water quality in Ganga river at the four selected cities. The result reveals that turbidity, dissolved oxygen and biochemical oxygen demand are the parameters that are most important in assessing variations of water quality. Water quality index based on eight parameters (turbidity, DO, BOD, COD, pH, TS, TSS and TDS) calculated for all four cities were found to range from medium to bad. Thus, this study illustrates the usefulness of multivariate statistical techniques for analysis, interpretation of complex data sets and understanding spatial variations in water quality for effective river water quality management.

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. Keywords: China, Spatial distributions of PM2.5, Long-term mortality, Morbidity, Economic loss

Ambient ozone (O3) pollution has become a big issue in China. Recent studies have linked long- and short-term O3 exposure to several public health risks. In this study, we (1) characterize the long-term and short-term O3-attributed health metric in China from 2015 to 2019; (2) estimate the surface O3 trends; and (3) quantify the long-term and short-term health impacts (i.e. all-cause, cardiovascular and respiratory mortality) in 350 urban Chinese cities. In these 5-years, the national annual average of daily maximum 8 h average (AVGDMA8) O3 concentrations and warm-season (April–September) 4th highest daily maximum 8 h average (4DMA8) O3 concentrations increased from 74.0 ± 15.5 μg/m3 (mean ± standard deviation) to 82.3 ± 12.0 μg/m3 and 167 ± 37.0 μg/m3 to 174 ± 30.0 μg/m3 respectively. During this period, the DMA8 O3 concentration increased by 1.9 ± 3.3 μg/m3/yr across China, with over 70% of the monitoring sites showing a positive upward trend and 19.4% with trends >5 μg/m3/yr. The estimated long-term all-cause, cardiovascular and respiratory premature mortalities attributable to AVGDMA8 O3 exposure in 350 Chinese cities were 181,000 (95% CI: 91,500–352,000), 112,000 (95% CI: 38,100–214,000) and 33,800 (95% CI: 0–71,400) in 2019, showing increases of 52.5%, 52.9% and 54.6% respectively compared to 2015 levels. Similarly, short-term all-cause, cardiovascular and respiratory premature mortalities attributed to ambient 4DMA8 O3 exposure were 156,000 (95% CI: 85,300–227,000), 73,500 (95% CI: 27,500–119,000) and 28,600 (95% CI: 14,500–42,800) in 2019, increases of 19.6%, 19.8% and 21.2% respectively compared to 2015. The results of this study are important in ascertaining the effectiveness of recent emission control measures and to identify the areas that require urgent attention.

Countries around the world introduced strict restrictions on movement and activities known as ‘lockdowns’ to restrict the spread of the novel coronavirus disease (COVID-19) from the end of 2019. A sudden improvement in air quality was observed globally as a result of these lockdowns. To provide insight into the changes in air pollution levels in response to the COVID-19 restrictions we have compared surface air quality data in Delhi during four phases of lockdown and the first phase of the restriction easing period (25 March to 30 June 2020) with data from a baseline period (2018–2019). Simultaneously, short-term exposure of PM2.5 and O3 attributed premature mortality were calculated to understand the health benefit of the change in air quality. Ground-level observations in Delhi showed that concentrations of PM10, PM2.5 and NO2 dropped substantially in 2020 during the overall study period compared with the same period in previous years, with average reductions of ~49%, ~39%, and ~39%, respectively. An overall lower reduction in O3 of ~19% was observed for Delhi. A slight increase in O3 was found in Delhi’s industrial and traffic regions. The highest peak of the diurnal variation decreased substantially for all the pollutants at every phase. The decrease in PM2.5 and O3 concentrations in 2020, prevented 904 total premature deaths, a 60% improvement when compared to the figures for 2018-2019. The restrictions on human activities during the lockdown have reduced anthropogenic emissions and subsequently improved air quality and human health in one of the most polluted cities in the world. Implication StatementI am submitting herewith the manuscript entitled “Unprecedented Reduction in Air Pollution and Corresponding Short-term Premature Mortality Associated with COVID-19 Forced Confinement in Delhi, India” for potential publishing in your journal.The novelty of this research lies in: (1) we utilized ground-level air quality data in Delhi during four phases of lockdown and the first phase of unlocking period (25th March to 30th June) for 2020 as well as data from the baseline period (2018–2019) to provide an early insight into the changes in air pollution levels in response to the COVID-19 pandemic, (2) Chatarize the change of diurnal variation of the pollutants and (3) we assess the health risk due to PM2.5 and O3. Results from ground-level observations in Delhi showed that concentrations of PM10, PM2.5 and NO2 substantially dropped in 2020 during the overall study period compared to the similar period in previous years, with an average reduction of ~49%, ~39%, and ~39%, respectively. In the case of O3, the overall reduction was observed as ~19% in Delhi, while a slight increase was found in industrial and traffic regions. And consequently, the highest peak of the diurnal variation decreased substantially for all the pollutants. The health impact assessment of the changes in air quality indicated that 904 short-term premature deaths (~60%) were prevented due to the decline in PM2.5 and O3 concentrations in the study period. The restrictions on human activities during the lockdown have reduced the anthropogenic emissions and subsequently improved air quality and human health in one of the most polluted cities in the world. Implication Statement I am submitting herewith the manuscript entitled “Unprecedented Reduction in Air Pollution and Corresponding Short-term Premature Mortality Associated with COVID-19 Forced Confinement in Delhi, India” for potential publishing in your journal. The novelty of this research lies in: (1) we utilized ground-level air quality data in Delhi during four phases of lockdown and the first phase of unlocking period (25th March to 30th June) for 2020 as well as data from the baseline period (2018–2019) to provide an early insight into the changes in air pollution levels in response to the COVID-19 pandemic, (2) Chatarize the change of diurnal variation of the pollutants and (3) we assess the health risk due to PM2.5 and O3. Results from ground-level observations in Delhi showed that concentrations of PM10, PM2.5 and NO2 substantially dropped in 2020 during the overall study period compared to the similar period in previous years, with an average reduction of ~49%, ~39%, and ~39%, respectively. In the case of O3, the overall reduction was observed as ~19% in Delhi, while a slight increase was found in industrial and traffic regions. And consequently, the highest peak of the diurnal variation decreased substantially for all the pollutants. The health impact assessment of the changes in air quality indicated that 904 short-term premature deaths (~60%) were prevented due to the decline in PM2.5 and O3 concentrations in the study period. The restrictions on human activities during the lockdown have reduced the anthropogenic emissions and subsequently improved air quality and human health in one of the most polluted cities in the world.

Long-term exposure to the ambient fine particulate matter (PM

The Chinese government, as a policy response, has continued to invest efforts and resources to implement cost-effective air pollution control technologies and stringent regulation to reduce emissions from the most contributing sectors to protect the environment and public health. The higher density of monitoring stations (> 1600) distributed across China provides a timely opportunity to use them to study in detail the national pollution trends in light of more stringent air pollution control policies. In the present study, air quality datasets comprising hourly concentrations of PM2.5, O3, NO2, and SO2 collected across 1309, 1341, 1289, and 1347 monitoring stations respectively were obtained from the National Environmental Monitoring Centre over 4 years (2015–2018) and trend analysis was performed. Results indicate that the overall annual trends for PM2.5 and SO2 were − 2.9 ± 2.7 and − 3.2 ± 3.2 μg/m3/year, while the winter trends were − 4.8 ± 5.8 and − 6.9 ± 8.4 μg/m3/year respectively across China. The daily maximum 8-h average (DMA8) ozone concentration showed a significant positive trend of 2.4 ± 4.6 μg/m3/year, which was comparatively higher in summer at 4.4 ± 9.0 μg/m3/year. On the other side, NO2 trend is not great in number (− 0.45 ± 2.0 μg/m3/year). Overall, 62.2%, 61.8%, and 20.9% of PM2.5, SO2, and NO2 monitoring stations were associated with a negative trend of ≥ − 2 μg/m3/year. For O3 DMA8 concentrations, 50.7% of the monitoring stations showed a significant positive trend of ≥ 2 μg/m3/year. In light of the Chinese government’s increasing impetus on combating air pollution and climate change via new policy regulations, it is important to understand the spatio-temporal distributions and relative contributions of the spectrum of gaseous pollutants to the pollution loads as well as identify changing emission loads across sectors. The results of this study will facilitate the formulation of evidence-based air pollution reduction strategies and policies.

In past decade of rapid industrial development and urbanization, China has witnessed increasingly persistent severe haze and smog episodes, posing serious health hazards to the Chinese population, especially in densely populated cities. Quantification of health impacts attributable to PM2.5 (particulates with aerodynamic diameter

The impacts of COVID-19 lockdown restrictions have provided a valuable global experiment into the extent of improvements in air quality possible with reductions in vehicle movements. Mexico City, London and Delhi all share the problem of air quality failing WHO guideline limits, each with unique situations and influencing factors. We determine, discuss and compare the air quality changes across these cities during the COVID-19, to understand how the findings may support future improvements in their air quality and associated health of citizens. We analysed ground-level PM10, PM2.5, NO2, O3 and CO changes in each city for the period 1st January to August 31, 2020 under different phases of lockdown, with respect to daily average concentrations over the same period for 2017 to 2019. We found major reductions in PM10, PM2.5, NO2 and CO across the three cities for the lockdown phases and increases in O3 in London and Mexico City but not Delhi. The differences were due to the O3 production criteria across the cities, for Delhi production depends on the VOC-limited photochemical regime. Levels of reductions were commensurate with the degree of lockdown. In Mexico City, the greatest reduction in measured concentration was in CO in the initial lockdown phase (40%), in London the greatest decrease was for NO2 in the later part of the lockdown (49%), and in Delhi the greatest decrease was in PM10, and PM2.5 in the initial lockdown phase (61% and 50%, respectively). Reduction in pollutant concentrations agreed with reductions in vehicle movements. In the initial lockdown phase vehicle movements reduced by up to 59% in Mexico City and 63% in London. The cities demonstrated a range of air quality changes in their differing geographical areas and land use types. Local meteorology and pollution events, such as forest fires, also impacted the results., Graphical abstract Image 1

This study evaluated the human health risk in terms of the excess number of mortality and morbidity in the megacity Mumbai, India due to air pollution. AirQ software was used to enumerate the various health impacts of critical pollutants in Mumbai in past 22 years during 1992-2013. A relationship concept based on concentration-response relative risk and population attributable-risk proportion was employed by adopting World Health Organization (WHO) guideline for concentrations of air pollutants like PM10, SO2 and NO2. For the year 1992 in Mumbai, it was observed that excess number of cases of total mortality, cardiovascular mortality, respiratory mortality, hospital admission due to COPD, respiratory disease and cardiovascular disease were 8420, 4914, 889, 149, 10568 and 4081 respectively. However, after 22 years these figures increased to 15872, 9962, 1628, 580, 20527 and 7905 respectively, but all of these reached maximum in the year 2006. From the result, it is also noted that except COPD morbidity the excess number of cases from 1992-2002 to 2003-2013 increased almost by 30%; and the excess number of mortality and morbidity is basically due to particulate matter (PM10) than due to gaseous pollutants.

Particulate air pollution is becoming a serious public health concern in urban cities in India due to air pollution-related health effects associated with disability-adjusted life years (DALYs) and economic loss. To obtain the quantitative result of health impact of particulate matter (PM) in most populated Mumbai City and most polluted Delhi City in India, an epidemiology-based exposure-response function has been used to calculate the attributable number of mortality and morbidity cases from 1991 to 2015 in a 5-year interval and the subsequent DALYs, and economic cost is estimated of the health damage based on unit values of the health outcomes. Here, we report the attributable number of mortality due to PM10 in Mumbai and Delhi increased to 32,014 and 48,651 in 2015 compared with 19,291 and 19,716 in year 1995. And annual average mortality due to PM2.5 in Mumbai and Delhi was 10,880 and 10,900. Premature cerebrovascular disease (CEV), ischemic heart disease (IHD), and chronic obstructive pulmonary disease (COPD) causes are about 35.3, 33.3, and 22.9% of PM2.5-attributable mortalities. Total DALYs due to PM10 increased from 0.34 million to 0.51 million in Mumbai and 0.34 million to 0.75 million in Delhi from average year 1995 to 2015. Among all health outcomes, mortality and chronic bronchitis shared about 95% of the total DALYs. Due to PM10, the estimated total economic cost at constant price year 2005 US$ increased from 2680.87 million to 4269.60 million for Mumbai City and 2714.10 million to 6394.74 million for Delhi City, from 1995 to 2015, and the total amount accounting about 1.01% of India's gross domestic product (GDP). A crucial presumption is that in 2030, PM10 levels would have to decline by 44% (Mumbai) and 67% (Delhi) absolutely to maintain the same health outcomes in year 2015 levels. The results will help policy makers from pollution control board for further cost-benefit analyses of air pollution management programs in Mumbai and Delhi.

The ground-level ozone (O3) concentration in the urban regions of China has become an increasingly noticeable environmental problem in recent years. Many epidemiological studies have reported the association between O3 pollution and mortality, only a few studies have focused on the O3-related mortality and corresponding economic effects at the Chinese city and province level. This study reports the seasonal variation of ground-level O3 in 338 cities of China during the year 2016 and evaluates its effect on premature mortality and economic loss. It further illustrates the differences in cause-specific mortality outcomes of the log-linear and linear model, two of the prominently used methods for estimating health effects. In 2016, the annual average daily maximum 8-h O3 concentration in China ranged between 74 and 201 μg/m3 (138 ± 24.7 μg/m3). 30% of the total population was exposed to >160 μg/m3 O3 concentration (Chinese national ambient air quality standard) and about 67.2% urban population lived in exposure above the WHO recommended O3 concentrations (100 μg/m3). The estimated national O3-attributable mortality was 74.2 × 103 (95% CI: 16.7 × 103–127 × 103) in the log-linear model, whereas, the total O3-related mortality using the linear model was 69.6 × 103 (95% CI: 16.2 × 103–115 × 103). The exposure to O3 caused a nationwide economic loss of about 7.6 billion US$ (range: 1.7–12.9) in 2016. This study uniquely provides most comprehensive coverage of the Chinese cities for O3 associated mortality utilizing ground level measurement data for 2016 and presents a measurable assessment to the policymakers of China for streamlining their efforts on air quality improvement and O3 containment.

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 PM

This paper presents an objective methodology for determining the optimum number of ambient air quality monitoring stations in a air quality monitoring network that will give maximum information of present air quality. The suggested two fold approach is a combination of fuzzy similarity measures and fuzzy c-mean (FCM) clustering. In the first stage, cosine amplitude—one of the fuzzy similarity measures is used to classify or to regroup monitoring stations for the most critical air pollutant PM10 and NO2 for the defined possibility (α-cut) levels. In the second stage, average values of PM10, NO2, SO2, CO and SPM for the winter months being a worst case scenario are used in FCM clustering. Finally, the optimal number of air quality monitoring locations is selected as those (1) which form single station partition (unique partition) in crisp relational matrix for PM10 and NO2 and (2) have highest membership in fuzzy clustering. The methodology has been demonstrated by applying to case study on Delhi Metro City in India. The air quality data of criteria pollutants was collected for 15 months from the installed 41 stations. The outcome of the study reveals that the city of Delhi needs only 16 monitoring stations which will result into sizable reduction in the capital cost and the recurring expenses in ambient air quality monitoring in Delhi.

This paper presents an objective methodology for determining the optimum number of ambient air quality stations in a monitoring network based on human health risk (HHR). The case study is related to HHRs in terms of mortality and morbidity (hospital admission) due to adverse effects of air pollution in 41 wards in Delhi in India, which were evaluated using Ri-MAP model developed by the World Health Organization (WHO). By adopting the World Health Organization (WHO) guideline concentrations of the air pollutants total suspended particles (TSP) or its surrogate, sulfur dioxide (SO2) and nitrogen dioxide (NO2), concentration-response relationships and a population attributable-risk proportion concept are applied. It was found that an average total mortality was 1150 [631-1852 at 95% confidence interval (CI)], cardiovascular mortality was 450 (440-665 at 95% CI), respiratory mortality was 170 (96-288 at 95% CI), and chronic obstructive pulmonary disease (COPD) morbidity was 176 (2-333 at 95% CI) for every one million population in Delhi according to 2008 population statistics. There were 20 wards which had mortality and morbidity higher than the average. It may be concluded that pollution control authorities need to monitor air quality in these 20 identified locations having high health risk more rigorously. From the result, it was also seen that the excess number of mortality and morbidity in Delhi was basically due to particulate matter (PM) than due to gaseous pollutants. Hence, the pollution control authorities in Delhi urgently need proper management policy to improve ambient air quality in terms of TSP levels.

Objectives of the present study are to provide quantitative estimations of air pollution health impacts and monetary burden on people living in Agra city, the fourth most populated city in Uttar Pradesh, India. To estimate the direct health impacts of air pollution in Agra city during year 2002 to 2014, ‘Risk of Mortality/Morbidity due to Air Pollution’ model was used which is adopted from air quality health impact assessment software, developed by world health organization (WHO). Concentrations of NO2, SO2 and PM10 have been used to assess human health impacts in terms of attributable proportion of the health outcome as- annual number of excess cases of total mortality, cardiovascular mortality, respiratory mortality, hospital admission chronic obstructive pulmonary disease (COPD), hospital admission respiratory disease and hospital admission cardiovascular disease and it was observed that attributable number of cases were 1325, 908, 155, 138, 1230 and 348 respectively in year 2002. However, after thirteen years these figures increased to 1607, 1095, 189, 167, 1568 and 394 respectively. From these results, it was observed that from 2002 to 2014, the attributable number of cases increased almost by 13.43 to 27.52%. As a result, the monetary cost burden due to air pollution related health effects also increased very highly; it was 67.99 million US$ in 2002, which transformed into 254.52 million US$ in 2014. In future, if air quality continues to follow current pollutant concentration trend, the monetary cost burden will reach a level of US$ 570.12 million in year 2020, which is not only a thoughtful matter but also a threatful matter and it signifies the importance of rectification measures for air quality in Agra city.

The objective of designing and installation air quality monitoring network (AQMN) is to reduce network density with a view to acquire maximum information on air quality with minimum expenses. In spite of the best research efforts, there has been no general acceptance of any method for deciding the number of stations. Majority of the cities have, therefore, installed monitoring stations with their own guidelines. The present paper presents a useful formulation for classification of the existing air quality monitoring stations (AQMS) using fuzzy similarity measures. The case study has been demonstrated by applying the methodology to the already-installed AQMS in Delhi, India.