Air Quality Index Values Research Articles

Assessment of spatial variations of particulate matter (PM10 and PM2.5) in Bahrain identified by air quality index (AQI)

The rapid urbanization, industrialization, modernization, and the frequent Middle Eastern dust storms have negatively impacted the ambient air quality in Bahrain. The objective of this study is to identify the most critical atmospheric air pollutants with emphasis on their potential risk to health based on calculated AQI (air quality index) values using EPA approach. The air quality datasets of particulate matters (PM10 and PM2.5), ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) were measured in January 2012 and August 2012 using five mobile air quality monitoring stations located at different governorates. The results of this study demonstrated that PM10 and PM2.5 are the most critical air pollutants in Bahrain with PM2.5 prevailing during January 2012 and PM10 prevailing during August 2012. The corresponding AQI categories were utilized to evaluate spatial variability of particulate matters in five governorates. The impact of meteorological factors such as ambient air temperature, wind speed, relative humidity, and total precipitation on ambient air quality were discussed. The analysis demonstrated that the highest PM10 concentrations were observed in the Northern Governorate while the highest PM2.5 concentrations were observed in the Capital, Central, and Northern Governorates during August 2012. It was observed that the levels of PM2.5 pollution were higher within proximity of the industrial zone. The results suggested that the average PM2.5/PM10 ratio in August 2012 was lower than in January 2012 due to the Aeolian processes. This study concludes that higher wind speed, total precipitation, relative humidity rates, and lower ambient air temperature in January 2012 assisted with the dissipation of particulate matter thus lowering the pollution levels of both PM10 and PM2.5 in comparison to August 2012.

Spatial variation of air quality index and urban driving factors linkages: evidence from Chinese cities.

Daily air quality index (AQI) of 161 Chinese cities obtained from the Ministry of Environmental Protection of China in 2015 is conducted. In this study, to better explore spatial distribution and regional characteristic of AQI, global and local spatial autocorrelation is utilized. Pearson's correlation is introduced to determine the influence of single urban indicator on AQI value. Meanwhile, multiple linear stepwise regression is chosen to estimate quantitatively the most influential urban indicators on AQI. The spatial autocorrelation analysis indicates that the AQI value of Chinese 161 cities shows a spatial dependency. Higher AQI is mainly located in north and northwest regions, whereas low AQI is concentrated in the south and the Qinghai-Tibet regions. The low AQI and high AQI values in China both exhibit relative immobility through seasonal variation. The influence degree of three adverse urban driving factors on AQI value is ranked from high to low: coal consumption of manufacturing > building area > coal consumption of the power industry. It is worth noting that the risk of exposed population to poor quality is greater in the northern region than in other regions. The results of the study provide a reference for the formulation of urban policy and improvement of air quality in China.

Characteristics of atmospheric PM2.5 in stands and non-forest cover sites across urban-rural areas in Beijing, China

Monitoring air pollution at a city scale is essential for controlling urban air pollution in cities, especially megacities, in China. In this paper, data for 1 city center site and 5 paired (stands and non-forested) PM2.5 monitoring sites across urban-rural areas in Beijing were collected, and air PM2.5 and influential factors were analyzed. The results showed that the mean value of PM2.5 (February to December 2013) was lower in the stands (67.00 μ gm �3 ) than in the non-forested sites (78.02 μ gm �3 ). The diurnal variation was a bimodal double-dip type, and the average daily PM2.5 was lower in the vegetation areas than in the non-vegetation areas in both Changping Dingling (vegetation) and Changping town (non-vegetation) and showed day > night. In the typical pollution process, accompanied by an increase or decrease in PM2.5 and air quality index values, the higher the PM2.5, the lower the wind speed. PM2.5 in the vegetation areas lagged behind PM2.5 in the non-vegetation areas on typical sunny days but not on typical rainy and windy days. On typical sunny days, the variation of PM2.5 in the vegetation areas was less than that in the non-vegetation areas. PM2.5 did not change significantly under rainy conditions and before a rain, but the variation was smaller after a

The Air Quality, Characterization of Polycyclic Aromatic Hydrocarbon, Organic Carbon, and Diurnal Variation of Particulate Matter over Riyadh City

Concentrations of airborne particulate matter (PM10 & PM2.5) were measured over a period extended from January 2012 to October 2013 at two sites: a suburban area, Riyadh airport (AP) and an urban area, King Fahd road district (KF) (densely trafficked site) in Riyadh city, Saudi Arabia. The quality of ambient air using the U.S. Environmental Protection Agency (USEPA) Air Quality Index (AQI) was evaluated during this period. The average AQI values exceeded 100 on 6 months out of 11 in AP district. At KF district the condition was worse, and AQI values exceeded 100 on all months. This emphasizes the fact that PM concentrations over Riyadh can be regarded as a real environmental problem that could be a severe risk to quality of life and threaten human health. A higher-volume air sampler at the two sites was used to collect particulate matter (PM10 and PM2.5) for chemical analysis of the PM for PAHs and total organic carbon. At KF station the mean concentrations’ values of total PAHs in PM2.5 and PM10 were 4.89 ng/m3 and 12.96 ng/m3 respectively. At the AP station the corresponding values of total PAHs in PM2.5 and PM10 were 8.07 ng/m3 and 11.91 ng/m3 respectively. The PAHs did not exceed the air criteria of World Health Organization indicating the air pollution of PAHs in Riyadh District was not serious. As for the total organic carbon the results demonstrated that the concentration ranged from 0.211 to 0.506 g/kg at KF station and from 0.054 to 0.342 g/kg at AP station. On an annual basis, OC concentrations were two times higher for the city center site (KF) compared to the rural AP site. This can most likely be explained by increased emissions from higher traffic at KF site. The diurnal variability of PM was mainly associated with the severity of pollution.

Investigation of Impacts of Mining and Transportation Activities on Ambient Air Quality of Dhanbad City

The present study was intended to emphasize the assessment of ambient air quality of Dhanbad city with respect to PM10, PM2.5, SO2 and NOX concentrations, in order to investigate the impact of mining and transportation activities. From the monitoring and analysis at four selected monitoring stations during winter and summer seasons, significant spatial variation in pollutant (PM10, PM2.5, SO2 and NOX) concentrations is quite evident. The concentrations of PM10 were observed highest in mining area (at Dhansar PS; 291 µg/m3), whereas the PM2.5 the concentrations were observed higher along traffic routes (especially, at Bank More; 218 µg/m3). Higher concentratios of PM10 in mining area indicates the substantial impact of dust emanated from mining and associated activities on air quality. Whereas, the higher PM2.5 concentration along the transportation routes shows the influence of transportation activities on the airshed of the area. The significant seasonal variation in pollution levels is also apparent, as the concentrations of every pollutant were observed higher during the winter, than the summer season, at all sites. The mean concentration levels of PM10 and PM2.5 were observed 267 µg/m3, 173 µg/m3 and 234 µg/m3, 108 µg/m3 during winter and summer seasons, respectively. From the calculated values of air quality index, it is evident that Dhansar PS and Bank More are most polluted sites and PM10 is the most alarming pollutant in the area under investigation.

The Spatial-Temporal Characteristics of Air Pollution in China from 2001-2014.

To provide some useful information about the control of air pollution in China, we studied the spatial-temporal characteristics of air pollution in China from 2001–2014. First, we drew several line charts and histograms of the Air Pollution Index (API) and Air Quality Index (AQI) of 31 capital cities and municipalities to research the distribution across different times and cities; then, we researched the spatial clustering of API and AQI; finally, we examined the shift of the gravity center of API and AQI in different years and months. The API values had a decreasing trend: the high values had a clustering trend in some northern cities, and the low values had a clustering trend in some southern cities. The AQI values were relatively low, from 15:00–17:00 during the day. The gravity center of API had a trend of moving south from 2001–2003, then fluctuated in an unordered pattern and moved north in the winter. The AQI gravity center did not have a regular shift during different months. In conclusion, the government should take action to mitigate air pollution in some typical cities, as well as air pollution during the winter.

Estimating the Impact of Urbanization on Air Quality in China Using Spatial Regression Models

Urban air pollution is one of the most visible environmental problems to have accompanied China’s rapid urbanization. Based on emission inventory data from 2014, gathered from 289 cities, we used Global and Local Moran’s I to measure the spatial autorrelation of Air Quality Index (AQI) values at the city level, and employed Ordinary Least Squares (OLS), Spatial Lag Model (SAR), and Geographically Weighted Regression (GWR) to quantitatively estimate the comprehensive impact and spatial variations of China’s urbanization process on air quality. The results show that a significant spatial dependence and heterogeneity existed in AQI values. Regression models revealed urbanization has played an important negative role in determining air quality in Chinese cities. The population, urbanization rate, automobile density, and the proportion of secondary industry were all found to have had a significant influence over air quality. Per capita Gross Domestic Product (GDP) and the scale of urban land use, however, failed the significance test at 10% level. The GWR model performed better than global models and the results of GWR modeling show that the relationship between urbanization and air quality was not constant in space. Further, the local parameter estimates suggest significant spatial variation in the impacts of various urbanization factors on air quality.

Seasonal size distribution and possible health implications of atmospheric aerosols collected from a rural site of eastern central India

This paper contributes for the first time the seasonal mass size distribution of atmospheric aerosols and their possible health implications in a rural area of eastern central India. Size-segregated atmospheric aerosols were collected from July 2012 to June 2013 at rural site near Mahanadi riverside basin of Rajim (20° 59′N and 81°55′E), Chhattisgarh, India using nine-stage cascade impactor. Bimodal size distribution was found with stable peaks at 0.4–0.7 μm (fine mode) and 4.4–5.8 μm (coarse mode) during monsoon, winter, spring and summer seasons at study site. The mass median aerodynamic diameter of total impactor particle sizes was shifted from lower particle size in winter to higher particle size in summer. High concentrations of size-segregated aerosols were found during winter season with 45%, 55% and 36% of PM2.5–10, PM2.5 and PM1, respectively of the total PM10 aerosol. One unique observation was that the mass concentration of particulate matter increases abruptly in May and June during summer season, which was due to in situ burning of rice crop residues. The concentrations of upper respiratory tract and lungs particles were found to be highest during winter whereas respiratory airways particles showed maxima during summer season. The highest numbers of unfavorable days (i.e. value of air quality index > 101) were also observed during winter followed by summer season. The significant positive correlations found among particle in fine size bins (<0.43–2.5 μm) during winter and summer season was mainly due to the biomass burning activities during the study period at a rural site in eastern central India.

Accuracy of quantification of risk using a single-pollutant Air Quality Index.

Health risks associated with short-term exposure to ambient air pollution are communicated to the public by the US EPA through the Air Quality Index (AQI), but it remains unclear whether the current regulatory-based, single-pollutant AQI fully represents the actual risks of air pollution-related illness. The objective of this study is to quantify cardiovascular hospital admissions attributable to PM2.5 at each AQI category. Based on National Ambient Air Quality Standards (NAAQS), the highest AQI value among criteria pollutants (driver pollutant) is reported daily. We investigated excess cardiovascular hospital admissions attributable to fine particulate matter (PM2.5) exposure from 2000 to 2010 in Bronx, Erie, Queens, and Suffolk counties of New York. Daily total, unscheduled cardiovascular hospital admissions (principal diagnosis) for individuals aged 20-99 years, concentration-response functions for PM2.5, and estimated quarterly effective daily concentrations were used to calculate excess cardiovascular hospital admissions when PM2.5 was reported as the driver pollutant and when PM2.5 was not reported as the driver pollutant at each AQI category. A higher proportion of excess hospital admissions attributable to PM2.5 occurred when PM2.5 was the driver pollutant (i.e., ~70% in Bronx County). The majority of excess hospital admissions (i.e., >90% in Bronx County) occurred when the AQI was <100 ("good" or "moderate" level of health concern) regardless of whether PM2.5 was the driver pollutant. During the warm season (April-September), greater excess admissions in Suffolk County occurred when PM2.5 was not the AQI driver pollutant. These results indicate that a single-pollutant index may inadequately communicate the adverse health risks associated with air pollution.

Air Quality Index and its Possible Impact on Human Health in Industrial Area Gajraula, U.P.

Over the last decades the development of Gajraula as an industrial area has elevated the risk of atmospheric pollution. Thus to know the quality of air, AQI was evaluated and for the purpose PM 10 , PM 2.5 , SO 2 and NO 2 was monitored over a period of monsoon (July to September 2014) and winter season (November to January 2015) at three different Sites i.e. Raunaq Automotive (S1), Indra Chowk (S2) and Town Basti (S3). Results are based on AQI calculator recently launched by CPCB MoEF, New Delhi. The experimental results of air quality index (AQI) obtained from different Sites are 137 at S1, 144 at S2 and 95 at S3 in monsoon and 197 at S1, 268 at S2 and 131 at S3 in winter season. The highest AQI value was obtained from experimental result at Site S2 in winter season while lowest at Site S3 in monsoon season. Elevated concentration of all the pollutants at Site S2 may be due to anthropogenic activities i.e. industrial growth, vehicular density and other developmental works in this area. There are relatively stronger inter- pollutant correlation at the sites and results also suggest that a large number of industrial developments induces high correlation of PM 2.5 , PM 10 , NO 2 and SO 2 at S1 and S2 sites. Human beings are more affected as evidenced in increases in daily hospital admissions in different departments' i.e. Cardiac, Respiratory, Skin and Eye of Gajraula Government Hospital.

Impact of fine particulate fluctuation and other variables on Beijing's air quality index.

We analyzed fluctuation in Beijing's air quality over 328 days, based on air quality grades and air quality data from 35 atmospheric monitoring stations. Our results show the air over Beijing is subject to pollution 152 days of the year, or 46.34%. Among all pollutants, fine particulates, solid or liquid, 2.5 μm or less in size (PM2.5), appeared most frequently as the primary pollutant: 249 days, or 76% of the sample year (328 days). Nitrogen dioxide (NO2) and coarse particulates (PM10) cause the least pollution, appearing only 7 and 3 days, or 2 and 1% of the sample year, respectively. In Beijing, fine particulates like PM2.5 vary seasonally: 154.54 ± 18.60 in winter > 145.22 ± 18.61 in spring > 140.16 ± 20.76 in autumn > 122.37 ± 13.42 in summer. Air quality is best in August and worst in December, while various districts in Beijing experience different air quality. To be specific, from south to north and from west to east, air quality tends to improve. Meteorological elements have a constraining effect on air pollutants, which means there is a linear correlation between the air quality index and humidity, rainfall, wind speed, and temperature. Under a typical pollution scenario, the higher the air quality index (AQI) value, the lower the wind speed and the greater the relative humidity; the lower the AQI value, the higher the wind speed and lower the relative humidity. Analysis of influencing factors reveals that the air pollution is mainly particulate matter produced by burning coal, vehicle emissions, volatile oils and gas, fast development of food services, emissions from the surrounding region, and natural dust clouds formed in arid areas to the northwest. Topography affects the distribution of meteorological conditions, in turn varying air quality over the region from one location to another. Human activities also exercise impact on urban air quality with dual functions.

Comparison of ARIMA Model and Exponential Smoothing Model on 2014 Air Quality Index in Yanqing County, Beijing, China

In order to study the changes of air quality index (AQI) in Yanqing County, Beijing, China and predict the trend of AQI value, this paper constructed a time-series analysis．A non-stationary trend is found, and the ARIMA (1, 1, 2) model and Holt exponential smoothing model are found to sufficiently model the data. In comparison of these two model fittings, the ARIMA modelling result are better than Holt modelling’s in terms of trend capturing and result MSE, and in this data it is better to apply the ARIMA model to predict the future AQI values.

Analysis of the Characteristics of Haze and its Pollutants in Wuhan

The climatic characteristics of haze and pollutants were analyzed based on surface observations obtained from Meteorological Observation Station and Environmental Monitoring Center Station from January, 2013 to March, 2014 in Wuhan. The high frequency of haze in Wuhan was from September to December, with following trend “winter&gt;autumn&gt;spring&gt;summer”. According to the manual observation, haze occurrence at 11:00 was highest. The value of Air Quality Index (AQI) was highest in January and December. The primary pollutant in Wuhan was PM2.5, secondly O3. Horizontal diffusion played an important role in concentration of pollutants. The highest concentration of PM2.5 converged around wind from north or east.

Effectiveness of Risk Communication Using a Single- Pollutant Air Quality Index

Background To communicate health risks associated with short-term changes in air pollution the US EPA reports air quality through the Air Quality Index (AQI). Based on National Ambient Air Quality Standards (NAAQS), the highest AQI value among the individual pollutants (driver pollutant) is reported daily to the general public. However, it remains unclear whether the single-pollutant AQI fully represents the actual risks of air pollution related illness. Methods Daily fine particulate matter (PM2.5) concentrations and AQI values from 2000-2010 in Bronx, Erie, Queens and Suffolk Counties of New York were used to estimate quarterly PM2.5 effective daily concentrations (EDC) at each AQI category (i.e. good, moderate, and sensitive). Estimates were made separately for days when PM2.5 was the driver pollutant vs. days when the AQI of other pollutants were reported. Daily counts of total cardiovascular hospital admissions (ICD-9 code 390-459), concentration-response functions for PM2.5, and the estimated quarte...

Concentration and size distribution of viable bioaerosols during non-haze and haze days in Beijing.

Accumulation of airborne particulate matter (PM) has profoundly affected the atmospheric environment of Beijing, China. Although studies on health risks have increased, characterization of specific factors that contribute to increased health risks remains an area of needed exploration. Chemical composition studies on PM can readily be found in the literature but researches on biological composition are still limited. In this study, the concentration and size distribution of viable airborne bacteria and fungi were determined in the atmosphere from May to July 2013 in Beijing, China. Samples were collected during non-haze days and haze days based on the value of air quality index (AQI) PM2.5. Multiple linear regression results indicated that concentrations of viable bioaerosol exhibited a negative correlation with PM2.5 (AQI) ranging from 14 to 452. There was a little difference in size distribution of bioaerosol between non-haze and haze days that all airborne bacteria showed skewed trends toward larger sizes and airborne fungi followed a Gaussian distribution. Spearman's correlation analysis showed that a fraction of bioaerosol with fine and coarse particles had negative and positive relations with PM2.5 (AQI), respectively. Moreover, the temporal variation of d g (aerodynamic diameter) of bioaerosol with PM2.5 (AQI) fluctuated from 9:00 to 21:00, which suggested that their deposition pattern would vary during a day. The primary research in this study implied that aerodynamic size variation should be considered in assessing the bioaerosol exposure during haze weather.

Air quality and thermal comfort levels under extreme hot weather

Meteorological (T and RH values) and air pollution data (PM10, NO2 and O3 concentrations) observed in Athens, Thessaloniki and Volos were analyzed to assess the air quality and the thermal comfort conditions and to study their synergy, when extreme hot weather prevailed in Greece during the period 2001–2010. The identification of a heat wave day was based on the suggestion made by the IPCC to define an extreme weather event. According to it, a heat wave day is detected when the daily maximum hourly temperature value exceeds its 90th percentile. This temperature criterion was applied to the data recorded at the cities center. Air quality was assessed at three sites in Athens (city center, near the city center, suburb), at two sites in Thessaloniki (city center, suburb) and at one site in Volos (city center), while thermal comfort conditions were assessed at the cities center. Mean pollution levels during the heat wave days and the non-heat wave days were calculated in order to examine the impact of the extreme hot weather on air quality. For this purpose, the distributions of the common air quality index and the exceedances of the air quality standards in force during the heat wave days and the non-heat wave days were also studied. Additionally, the variation of the daily maximum hourly value of Thom's discomfort index was studied in order to investigate the effect of extreme hot weather on people's thermal comfort. Moreover, the values of the common air quality index and Thom's discomfort index were comparatively assessed so as to investigate their synergy under extreme hot weather.

MEASUREMENT AND STATISTICAL ANALYSIS OF AMBIENT AIR QUALITY STATUS IN CUDDALORE REGION, TAMILNADU, INDIA

The present study deals with the assessment of ambient air quality at six different locations in Cuddalore region, Tamil Nadu, India. The 24 hour s average concentration of suspended particulate matter (SPM), nitrogen dioxide (NO 2 ) and sulphur dioxide (SO 2 ) were monitored at cuddalore region, Tamil Nadu, India. The 24 hour concentration of SPM, NO 2 and SO 2 were 132.34 - 197.45, 56.33 - 89.71 and 58.78 8 0.68 µg/m 3 respectively. In present study, it was observed that concentration of NO 2 ; SO 2 levels exceed the permissible limit of National Ambient Air Quality Standard (NAAQS) for residential area. Exposures of these pollutants (NO 2 and SO 2 ) above standard levels (NAAQS) causes serious health effects on human health, associated with cardiovascular and respiratory diseases, increased risk of premature birth morbidity and even mortality. There is a significant correlation between SPM with NO 2 (r = 0.53), and S O 2 (r = 0.46) with linear regression also analyzed. Air Quality Index (AQI) values are in order of S1 > S4 > S5 > S6 > S3> S2, and, it is clearly indicates that among six locations, Nellikuppam (S1) is highly polluted and Thiruvandipuram (S2) is moderately polluted. This is due to combustion

Effects of forest areas on air quality; Aras Basin and its environment

BackgroundIn the study, the Aras Basin and its environment, one of the most important hydrological basins of Turkey, was evaluated. In survey area, to determine the change of air quality, it was benefited from 23,770 pieces of hourly measured SO2 (Sulfur dioxide) and PM10 (particulate matter) concentration values for the December, January and February of 2009–2010 in which the pollution is at peak, by forming database in geographical information system (GIS), spatial analyze maps were attained. By comparing; maps showing attained numeral air quality and maps showing the spread of forest lands in the region, it was tried to determine the relation and interaction between air quality and forest lands.ResultsThe results indicated that the Air Quality Index (AQI) values were the lowest for the forest land in the months which mean that the forest land was the most convenient place for health. The increase the AQI, air pollution also increases. The results indicated that the air quality index changed from 1 to 4 within the region. In the forest areas, the AQI values for the months were the lowest. This indicated that the most suitable places for health are the places with a high forest coverage rates (76,50; 66,46 and 96,78%). There was no forest area within the region where the AQI values were the highest, so the risk was maximum, for the months.ConclusionsAuthorities should create new afforestation areas and rehabilitate degraded forest lands to limit air pollution by increasing the quality of urban life.

Impact of fireworks on ambient air quality: a case study

Fireworks activity causes short-term air pollution. The festival of Diwali is celebrated with firecrackers in India during October/November every year. The ambient air quality was assessed in Vadodara city, Gujarat state of India during Diwali festival for the consecutive years 2009, 2010, and 2011. During the festival day, the average concentration of PM10 was increased 35 times compared with a normal day before Diwali. Similarly, concentration of SO2 has increased 23 times and NOx has increased 3 times on the festival day. The diurnal pattern of the above pollutants showed an increase in the night. Air quality index calculated for 24 h average during Diwali 2009 in two stations exceeded a value of 125 indicating severe air pollution in Vadodara city. It is also confirmed that the yearly pollution load is getting increased in Vadodara city as the air quality index value has a gradual increase from 2009 to 2011. The good correlation coefficient between different pollutants confirmed the firework as the prime source of pollution. Based on PM10 concentration, the highest mortality rate was found to be 30.4 %. The short-term high accumulation of PM10 is a matter of serious concern for city dwellers as it can penetrate deep into the lungs and cause many respiratory and cardiovascular diseases.

INDOOR AIR QUALITY INDEX AND CHRONIC HEALTH DISEASE: A PILOT STUDY

Approximately about 2 million premature deaths per year in developing countries by indoor pollution th is is estimated by WHO (2006). Indoor activities (cooking, smoking, incens e, mosquitoes coil and others), building characteri stics and outdoor climatic conditions have affected indoor air quality environ ment. Especially young and unemployed people spend about 90% of their times in indoor environment so, air quality index (AQI) is n ow becomes the essential task for determine the qu ality of indoor homes. Indoor air quality index concern to pollutant concentratio n. AQI = 1/3 [SPM/SSPM + SO2/SSO2 + NOx/SNOx ] x 10 0. More will be the value of AQI more hazardous will be the quality. On e of the most important parameter for indoor air qu ality is particulate matter (PM2.5) and due to long exposure to particulate mat ters may lead to mortality. Women and children are mainly affected by indoor pollution because they spend more time in homes as compare to others. So, increased the respiratory sy mptoms, decreased the lung function, increased lung cancer incidence, cardiova scular mortality and accelerated artherosclerosis a nd vascular inflammation all these ill effects mainly observed in women’s and i nfants.