Statistical Air Pollution Index (API) for Trinidad and Tobago Based on Observed Data on Trinidad’s West Coast

Air pollution has been identified as the largest global environmental threat facing the world today, estimated to cause 7 - 10 million deaths worldwide annually (World Health Organisation, 2014, 2016;Yale University, 2018). Trinidad and Tobago, with a per capita GDP of USD$16310 (2019), is the most industrialised of the Caribbean islands, and like the rest of the Caribbean region is also affected by seasonal Sahara dust (PM2.5). Assessment of the air quality was done for over Trinidad’s west coast. Pollution was measured at four stations during March ‘15-May ‘16, representative of rural, urban, mixed background and industrial land uses. Annual mean PM2.5 and PM10 in ambient air exceeded the WHO guidelines for protection of public health (n = 522). PM2.5 and PM10 exceed the WHO (2006) safe limit guidelines (PM2.5 is 10 μg/m3;PM10 is 20 μg/m3) over 70% of the time sampled at urban and industrial sites. Gaseous pollutants found to be in exceedance were CO, NH3, NO2, N2O, C6H6. Nitrogen dioxide and benzene were the most prolific. A collated metric based on measurement of these pollutants yielded a statistically validated algorithm—An Air Pollution Index. The single metric can convey useful and easily understood information on air quality to the regulators and the general public.

Relationship between Air Pollution Index (API) and Crowd Health in Nanchang City

Objective: To explore the effect of Air Pollution Index (API) on people’s health. Methods: The data on air pollution index (API), NO2, SO2 and PM10 were based on the everyday monitoring information from environmental monitoring station of Nanchang City. The everyday outpatient service diseases information of 2005 related to air pollution from some First Level Hospitals in Nanchang city was collected, and was summarized and analyzed by statistics software of Excel 2003 and SPSS11.5. Results: The average concentrations of NO2, SO2 and PM10 in the air of Nanchang city from 2006-2009 were 19.70 ± 8.56 μg/m3, 44.60 ± 10.45 μg/m3, 62.30 ± 19.76 μg/m3 respectively. Tight relationship was detected between NO2, SO2 and PM10. Air pollution index (API) can better reflect the air pollution status of Nanchang city. There were positive correlations between API and number of outpatient service diseases, including cardiovascular disease, respiratory disease, ophthalmology disease and ear-nose-throat (ENT) disease in Nanchang city. Conclusion API was related to the number of outpatient service relative diseases.

Air Pollution Exposure Based on Nighttime Light Remote Sensing and Multi-source Geographic Data in Beijing

Air pollution is a problem that directly affects human health,the global environment and the climate.The air quality index(AQI)indicates the degree of air pollution and effect on human health;however,when assessing air pollution only based on AQI monitoring data the fact that the same degree of air pollution is more harmful in more densely populated areas is ignored.In the present study,multi-source data were combined to map the distribution of the AQI and population data,and the analyze their pollution population exposure of Beijing in 2018 was analyzed.Machine learning based on the random forest algorithm was adopted to calculate the monthly average AQI of Beijing in 2018.Using Luojia-1 nighttime light remote sensing data,population statistics data,the population of Beijing in 2018 and point of interest data,the distribution of the permanent population in Beijing was estimated with a high precision of 200 m×200 m.Based on the spatialization results of the AQI and population of Beijing,the air pollution exposure levels in various parts of Beijing were calculated using the population-weighted pollution exposure level(PWEL)formula.The results show that the southern region of Beijing had a more serious level of air pollution,while the northern region was less polluted.At the same time,the population was found to agglomerate mainly in the central city and the peripheric areas thereof.In the present study,the exposure of different districts and towns in Beijing to pollution was analyzed,based on high resolution population spatialization data,it could take the pollution exposure issue down to each individual town.And we found that towns with higher exposure such as Yongshun Town,Shahe Town and Liyuan Town were all found to have a population of over 200000 which was much higher than the median population of townships of51741 in Beijing.Additionally,the change trend of air pollution exposure levels in various regions of Beijing in 2018 was almost the same,with the peak value being in winter and the lowest value being in summer.The exposure intensity in population clusters was relatively high.To reduce the level and intensity of pollution exposure,relevant departments should strengthen the governance of areas with high AQI,and pay particular attention to population clusters.

The Establishment of a New Air Health Index Integrating the Mortality Risks Due to Ambient Air Pollution and Non-Optimum Temperature

A composite Air Health Index(AHI)is helpful for separately emphasizing the health risks of multiple stimuli and communicating the overall risks of an adverse atmospheric environment to the public.We aimed to establish a new AHI by integrating daily mortality risks due to air pollution with those due to non-optimum temperature in China.Based on the exposure-response(E-R)coefficients obtained from time-series models,the new AHI was constructed as the sum of excess mortality risk associated with air pollutants and non-optimum temperature in 272 Chinese cities from 2013 to 2015.We examined the association between the“total AHI”(based on total mortality)and total mortality,and further compared the ability of the“total AHI”to predict specific cardiopulmonary mortality with that of“specific AHIs”(based on specific mortalities).On average,air pollution and non-optimum temperature were associated with 28.23%of daily excess mortality,of which 23.47%was associated with non-optimum temperature while the remainder was associated with fine particulate matter(PM2.5)(1.12%),NO2(2.29%,),and O3(2.29%).The new AHI uses a 10-point scale and shows an average across all 272 cities of 6 points.The E-R curve for AHI and mortality is approximately linear,without any thresholds.Each one unit increase in“total AHI”is associated with a 0.84%increase in all-cause mortality and 1.01%,0.98%,1.02%,1.66%,and 1.71%increases in cardiovascular disease,coronary heart disease,stroke,respiratory diseases,and chronic obstructive pulmonary disease mortality,respectively.Cause-specific mortality risk estimates using the“total AHI”are similar to those predicted by“specific AHIs.”In conclusion,the“total AHI”proposed herein could be a promising tool for communicating health risks related to exposure to the ambient environment to the public.

Use of Ventilation-Index in the Development of Exposure Model for Indoor Air Pollution—A Review

In indoor environment, emission factor of the cooking fuel plays a vital role in determining correlation between exposure assessment and health effects. Both indoor and outdoor air pollution exposures are widely influenced by the ventilation status. An optimum control of the air change rate has also significant impact on the exposure pattern. A number of studies revealed that the indoor particulates and gaseous exposures, resulting from the combustion of various cooking fuels, are associated with significant adverse health effects on pregnant mothers and new born babies. The impacts of ventilation status on air pollution exposure in households’ kitchens or living rooms have not been explored enough. Except a few studies with concrete rooms, especially in industries, no other studies have been established on the correlation between the ventilation index and air pollution exposure. The intent of this review is to discuss reported findings focused on the ventilation and exposure to air pollution. This will obviously help better understanding to modulate exposure profile in household condition using simple tool of ventilation measurement.

An Evaluation of Air Pollution Tolerance Index and Anticipated Performance Index of Some Tree Species Considered for Green Belt Development: A Case Study of Nandesari Industrial Area, Vadodara, Gujarat, India

It is well renowned that trees have capacity to reduce the air pollution. It is mandatory to expand tree plantation in industrial area to minimize the threat of pollutants. For green belt development, it is necessary to use plants that are tolerant to air pollution. The present study includes Air pollution tolerance index (APTI) of selected plant species with the help of biochemical analysis. On the basis of APTI and some other socioeconomic and biological parameters of plants, Anticipated Performance Index (API) was calculated. Out of twelve species, Ficus benghalensis showed to be the most efficient among others. As per classification of API, Ficus religiosa tree species is classified into the moderate category. Based on the APTI and API, appropriate plant species for green belt development in industrial area were identified and recommended for mitigating the pollution.

Characterization of Air Pollution in Urban Areas of Yangtze River Delta,China

The hallmark of development in the Yangtze River Delta(YRD) of East China has been sprawling urbanization. However, air pollution is a significant problem in these urban areas. In this paper, we investigated and analyzed the air pollution index(API) in four cities(Shanghai, Nanjing, Hangzhou and Ningbo) in the YRD from 2001 to 2012. We attempted to empirically examine the relationship between meteorological factors and air quality in the urban areas of the YRD. According to the monitoring data, the API in Shanghai, Nanjing, Hangzhou slightly declined and that in Ningbo increased over the study period. We analyzed the inter-annual, seasonal, and monthly variations of API, from which we found that the air quality had different temporal changes in the four cities. It was indicated that air quality was poor in winter and spring and best in summer. Furthermore, different weather conditions affected air quality level. The wind direction was considered as an important and influential factor to air pollution, which has an impact on the accumulating or cleaning processes of pollutants. The air quality was influenced by the different wind directions that varied with seasons and cities.

TYPICAL WEATHER CHARACTERISTICS ASSOCIATED WITH AIR POLLUTION IN HONG KONG AREA

With the hourly data of Air Pollution Index （AP1） by Hong Kong Environmental Protection Department （HKEPD） during the 6 years of 2000 - 2005 and NCEP / NCAR reanalysis data of 2.5°× 2.5° wind and pressure fields, the characteristics of API in Hong Kong area and the impacts of typical weather characteristics on the air pollution in Hong Kong have been studied. The results are shown as follows. （1） The API exhibits obvious seasonal variability as the number of air pollution days increases by the year. For most of the local monitoring stations, it is the most from January to March, a little less from July to September and the least from April to June. （2） There are four typical types of weather situations that are responsible for the air pollution in Hong Kong： tropical cyclones, continental cold highs, transformed highs that have moved out to sea and low pressure troughs.

Trends of Urban Air Pollution in Zhengzhou City in 1996-2008

Urban air pollution is a commonly concerned environmental problem in the world. Identification of air quality trend using long-term monitoring data is helpful to understand the effectiveness of pollution control strategies. This study, using data from six monitoring stations in Zhengzhou City, analyzed the changing trend in concentrations of SO2, NOJNO2 and TSP/PM10 in 1996-2008, based on non-parametric Mann-Kendall test and Sen＇s slope estimator, and evaluated the comprehensive air pollution level using Multi-Pollutant Index （MPI）. It was found that the concen- tration of each pollutant exceeded obviously the World Health Organization （WHO） guideline value, but the changing trend varied： SO2 and NO2 were significantly increased mainly due to an increase in coal consumption and vehicle number, while NOx, TSP and PM10 decreased. The air pollution was serious, and differed markedly among the three functional regions： it is the most severe in the Industrial and Residential Area （IRA）, followed by the Transportation Hub and Business District （THBD）, and then the High-tech, Cultural and Educational Area （HCEA）. Different from NO2 concentration that had a similar change trend/rate among the function regions, the change rate of PM10 concentra- tion differed spatially, decreased much more obviously in THBD than other two regions. For the whole city, the com- prehensive air pollution level declined gradually, illustrating that the air quality in Zhengzhou was improved in the last decade.

Feed-Forward Artificial Neural Network Model for Air Pollutant Index Prediction in the Southern Region of Peninsular Malaysia

This paper describes the application of principal component analysis (PCA) and artificial neural network (ANN) to predict the air pollutant index (API) within the seven selected Malaysian air monitoring stations in the southern region of Peninsular Malaysia based on seven years database (2005-2011). Feed-forward ANN was used as a prediction method. The feed-forward ANN analysis demonstrated that the rotated principal component scores (RPCs) were the best input parameters to predict API. From the 4 RPCs, only 10 (CO, O3, PM10, NO2, CH4, NmHC, THC, wind direction, humidity and ambient temp) out of 12 prediction variables were the most significant parameters to predict API. The results proved that the ANN method can be applied successfully as tools for decision making and problem solving for better atmospheric management.

Mapping Air Quality Using Remote Sensing Technology: A Case Study of Nairobi County

Nairobi County experiences rapid industrialization and urbanization that contributes to the deteriorating state of air quality, posing a potential health risk to its growing population. Currently, in Nairobi County, most air quality monitoring stations use low-cost, inaccurate monitors prone to defects. The study’s objective was to map Nairobi County’s air quality using freely available remotely sensed imagery. The Air Pollution Index (API) formula was used to characterize the air quality from cloud-free Landsat satellite images i.e., Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI from Google Earth Engine. The API values were computed based on vegetation indices namely NDVI, TVI, DVI, and the SWIR1 and NIR bands on the QGIS platform. Qualitative accuracy assessment was done using sample points drawn from residential, industrial, green spaces, and traffic hotspot categories, based on a passive-random sampling technique. In this study, Landsat 5 API imagery for 2010 provided a reliable representation of local conditions but indicated significant pollution in green spaces, with recorded values ranging from -143 to 334. The study found that Landsat 7 API imagery in 2002 showed expected results with the range of values being -55 to 287, while Landsat 8 indicated high pollution levels in Nairobi. The results emphasized the importance of air quality factors in API calibration and the unmatched spatial coverage of satellite observations over ground-based monitoring techniques. The study recommends the recalibration of the API formula for characteristic regions, exploring newer satellite sensors like those onboard Landsat 9 and Sentinel 2, and involving key stakeholders in a discourse to develop a suitable Kenyan air quality index.

Ambient Air Pollution and Pulmonary Tuberculosis in Malaysia

Introduction: The possible impact of ambient air pollution exposure on the development of active tuberculosis (TB) remains obscure. This study investigated the potential role of ambient air pollution in activating pulmonary TB (PTB) compared to extrapulmonary TB (EPTB). Materials and Methods: Data on TB cases were obtained from national surveillance data in Malaysia during 2013 and air pollution data were obtained from 52 air-monitoring stations around the country for the 3-year period of 2011-2013. Analyses were performed to estimate the odds of PTB vs. EPTB with changes in the 3-year (2011-2013) average Air Pollutant Index (API) and specific ambient air pollutants. Results: Results showed that the 95th-percentile of API levels during 2011-2013 was moderate and it was not associated with PTB. However, the odds of active PTB compared to EPTB was significantly elevated with the 95th-percentile levels for particulate matter with an aerodynamic diameter of 10 μm or less (aOR = 1.006, 95% CI: 1.002, 1.011), p-value Conclusions: These results provide suggestive evidence of the effects of ambient air pollution on development of active pulmonary TB compared to extrapulmonary TB. Additional research on the impacts of ambient air pollution on TB is warranted.

Air Pollution Interpolation Analysis Based on Measured and Simulated Sites in Nanjing

Based on the statistical analysis of API （air pollution index）, the study improves the layout of the site in the downtown of Nanjing and the surroundings. Through selecting more relevant factors to establish the API regression equation and making the inversion of API data in simulated sites, the interpolation values of API in both actual sites and simulated sites have been calculated. The methods include IDW （inverse distance weighting） interpolation, Spline interpolation, and Kriging interpolation Spherical model, Exponential model and the Gaussian model. Meanwhile, through the cross-validation to test the results of interpolation in different models or parameters, the study also obtains the best fit of the interpolation model or parameters. In addition, IDW p = 3, fitting coefficient of 0.644; Spline interpolation w = 1, the fitting coefficient of 0.972; Kriging interpolation, Gaussian, fitting coefficient of 0.684. The study indicates that in best fitting model, the parameters after in increasing the simulated site are not in line with the ones previous. The result shows that it is best to test different data separately and select the appropriate interpolation model, but not blindly use the same spatial interpolation. After the increasing of the stimulated site, the API estimated results in three interpolation methods are consistent with the spatial distribution trend. In the aspect of calculating the range, the improvement close the results between 3 interpolation methods and increase of the stimulated sites, and the values of Spline interpolation and Kriging interpolation is closer.

Time Series Analysis and Forecasting of the Air Quality Index of Atmospheric Air Pollutants in Zahleh, Lebanon

During the last decades, air pollution has become a serious environmental hazard. Its impact on public health and safety, as well as on the ecosystem, has been dramatic. Forecasting the levels of air pollution to maintain the climatic conditions and environmental protection becomes crucial for government authorities to develop strategies for the prevention of pollution. This study aims to evaluate the atmospheric air pollution of the city of Zahleh located in the geographic zone of Bekaa. The study aims to determine a relationship between variations in ambient particulate concentrations during a short time. The data was collected from June 2017 to June 2018. In order to predict the Air Quality Index (AQI), Naïve, Exponential Smoothing, TBATS (a forecasting method to model time series data), and Seasonal Autoregressive Integrated Moving Average (SARIMA) models were implemented. The performance of these models for predicting air quality is measured using the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and the Relative Error (RE). SARIMA model is the most accurate in prediction of AQI (RMSE = 38.04, MAE = 22.52 and RE = 0.16). The results reveal that SARIMA can be applied to cities like Zahleh to assess the level of air pollution and to prevent harmful impacts on health. Furthermore, the authorities responsible for controlling the air quality may use this model to measure the level of air pollution in the nearest future and establish a mechanism to identify the high peaks of air pollution.

Particulate Matter-Based Air Quality Index Estimate for Abuja, Nigeria: Implications for Health

In recent years, urban air quality in developing countries such as Nigeria has continued to degenerate and this has constituted a major environmental risk to human health. It has been shown that an increase in ambient particulate matter (PM10) load of 10 μg/m3 reduces life expectancy by 0.64 years. Air Quality Index (AQI) as demonstrated in this study shows how relatively clean or polluted the boundary layer environment of any location can be. The study was designed to measure the level of suspended particulate matter (PM2.5 and PM10) for dry and wet seasons, compute the prevalent air quality index of selected locations in Abuja with possible health implications. Suspended particulate matter (PM2.5 and PM10) was assessed using handheld aerosol particulate sampler. The US Oak Ridge National AQI was adopted for the eleven (11) locations sampled and monitored. The study results showed that the air quality of the selected areas in Abuja were generally good and healthy. Dry season, assessments, showed 15 - 95 μg/m3 and 12 - 80 μg/m3 for PM2.5 and PM10, respectively. While in wet season, 09 - 75 μg/m3 and 07 - 65 μg/m3 were recorded for PM2.5 and PM10. However at Jebi Central Motor Park, there was light air contamination with AQI of 42 for dry season and 31 for wet season. Other locations had clean air with AQI ≤ 11. It is revealed that clean air exists generally during the wet season. Comparing study outcome to other cities in Nigeria, residents of Abuja are likely not to be affected with health hazards of particulate matter pollution. Nonetheless, the high range of PM2.5 and PM10 (fine and coarse particles) ratio evaluated i.e., 1.06 - 1.79 was higher than the WHO recommended standard of 0.5 - 0.8. This ratio remains a health concerns for sensitive inhabitants like pregnant women and their foetus as well as infants below age five whose respiratory airways are noted to have high surface areas and absorption capacity for fine particulate matter. Vegetation known to absorb suspended particulate matter should be planted across Abuja metropolitan areas and air quality monitoring stations installed at strategic locations for continuous monitoring and evaluations.

Research and Analysis of a Persistent Heavy Pollution Event in Ningbo, China

From 7 to 12 January 2015, there was a rare persistent severe pollution event in Ningbo. Based on the data from routine weather observation, automatic weather station in Zhejiang Province and urban pollutant monitoring in Ningbo City, by using the particle backward trajectory analysis of NOAA HYSPLIT4 model, the diagnosis and analysis of the pollution development and dissipation process were carried out. The results show that: 1) Pollutants carried by cold air are main reason for pollution in the first stage of the pollution process. The transition from near-surface northwest wind to north-northeast wind is the key to air improvement;2) Favorable atmospheric circulation is the important reason for the long-term persistence of pollution. The long-term control of high-pressure center and low-level inversion between the two cold air effects make the boundary layer particularly stable, while the low horizontal wind speed is beneficial to polluting particles. With the continuous accumulation, the contaminated particles can not effectively diffuse either vertically or horizontally;3) In the late period of January 11, the supplement of weak cold air in the East Road made the air quality index (AQI) climb to about 300 again. The two advection conveyances of cold air to pollutant particles and the long-term stable maintenance of weather conditions conducive to the accumulation of pollutant particles are two important reasons for this rare and sustained heavy pollution event.

Spatio-Temporal Air Pollutant Characterization for Urban Areas

Urban pollution has now become increasingly recognized as an important determinant of air pollution in developed countries. The effect of urban air pollution in developing countries, on the other hand, has not been adequately addressed in the data Spatio-temporal time series. Thus, this study was intended to characterize the effect of urbanization on air pollution for an urbanized Klang Valley, Malaysia using Spatio-temporal data from 2008 to 2017. The Air Pollution Index (API) data and local pollutant concentration were employed to establish the links between urban air pollution. The analysis will be supported by determining the source of pollutants during the study period using Principal Component Analysis (PCA). The study identified that Carbon monoxide (CO), Nitrogen Dioxide (NO2), and Ozone (O3) are the major air pollution that has contributed to degrading air quality in the Klang Valley due to the vehicles, combustion process, and industries.

Air Quality Estimation Using Nonhomogeneous Markov Chains: A Case Study Comparing Two Rules Applied to Mexico City Data

A nonhomogeneous Markov chain is applied to the study of the air quality classification in Mexico City when the so-called criterion pollutants are used. We consider the indices associated with air quality using two regulations where different ways of classification are taken into account. Parameters of the model are the initial and transition probabilities of the chain. They are estimated under the Bayesian point of view through samples generated directly from the corresponding posterior distributions. Using the estimated parameters, the probability of having an air quality index in a given hour of the day is obtained.

中国城市PM_(2.5)污染暴露不平等及其驱动因素——基于Theil指数和LMDI分解

聚焦大气污染物PM_(2.5),对2015~2021年污染物浓度进行人口规模赋权,构建中国城市PM_(2.5)污染暴露水平指标,基于GINI系数和Theil指数测度污染暴露不平等,并按城市行政层级、省份进行差异分解;基于对数平均迪氏指数分解法(LMDI),分别对污染暴露水平、不平等程度进行驱动因素分解,探究其变化背后的社会经济因素.结果表明:中国城市PM_(2.5)污染暴露不平等总体温和,GINI系数保持在0.2以内且呈递减趋势.不同行政级别城市间未表现出污染暴露不平等,而省际差异对污染暴露不平等的贡献达68.4%;总体上能源的污染暴露乘数、政府绿色支出能耗强度是PM_(2.5)污染暴露水平的主要抑制因素,对污染暴露水平的驱动贡献超过60%.政府支出结构、支出规模、经济发展和人口因素对PM_(2.5)污染暴露起促增作用,但各因素在不同层级的城市间具有异质性;能源的污染暴露乘数、绿色支出能耗强度对不平等的动态抑制作用弱化,主要依靠能源绿色化的治污策略面临挑战.据此提出更好发挥政府作用,持续降低中国PM_(2.5)污染暴露水平,实现环境公共服务更加均等化的政策启示.

API法在马鞍山市空气质量评价中的应用

The air environmental quality of Ma′anshan City in Anhui Province was investigated by using of the Air Pollution Index (API) method. The result showed that the air environmental quality at Ma’anshan City during 1997-2000 was Ⅱ-Ⅰ grade (the air quality is just good), and the critical pollutants were TSP or NOx. Compared with other methods, the usage of API method may accurately reveal and easily calculate the degree of air pollution, and let the local residences and environmental personnel clearly understand the circumstance of air pollution.