Investigating Correlations and Variations of Air Pollutant Concentrations under Conditions of Rapid Industrialization – Kocaeli (1987–2009)

In this study, it was aimed to characterize temporal variations of air pollutants for determining contribution to pollution episodes and to obtain correlations between these pollutants. With this aim we used data analysis for measured sulfur dioxide (SO₂), particulate matter (PM, black fume and PM10), nitrogen oxides (NO_x), ozone (O₃), carbon monoxide (CO), methane (CH₄), and non‐methane hydrocarbons (NMHC) recorded in Kocaeli, one of the most industrilizated cities of Turkey. Pollutant concentrations were the results of continuous and semi‐automatic measurements. Semi‐automatic measurements of SO₂ and PM (black fume) were enclosing period from 1987 to 2008 whereas continuous monitoring of all pollutants included years of 2007–2009. In the first stage of the study daily, monthly, annual, and seasonal variations of pollution were researched. Annual average concentrations were compared with limits set by Air Quality Protection Regulation (AQPR), Air Quality Evaluation and Management Regulation (AQEMR), World Health Organization (WHO), European Union (EU), and National Ambient Air Quality Standards (USEPA). In the following stage relationships between pollutants such as NO₂–O₃, NO_x–CO, NO_x–NMHC, and NO_x–SO₂ were investigated and correlation coefficients were determined as 0.87, 0.56, 0.51, and 0.69, respectively. R² values of regression models developed from these correlations were 0.78, 0.56, 0.34, and 0.72, respectively. Vehicle density of the traffic was evaluated with NO_x–O₃ emissions and decrease was seen in NO_x emissions due to decreasing vehicle density at weekends whereas O₃ concentrations increased. These correlations enable prediction of the parameters that cannot be measured which is important for providing improvement in early warning systems.     

Estimation of PM<Subscript>10</Subscript> concentration from air quality data in the vicinity of a major steelworks site in the metropolitan area of Avilés (Northern Spain) using machine learning techniques

Atmospheric particulate matter (PM) is one of the pollutants that may have a significant impact on human health. Data collected over 7 years from the air quality monitoring station at the LD-III steelworks, belonging to the Arcelor-Mittal Steel Company, located in the metropolitan area of Avilés (Principality of Asturias, Northern Spain), is analyzed using four different mathematical models: vector autoregressive moving-average, autoregressive integrated moving-average (ARIMA), multilayer perceptron neural networks and support vector machines with regression. Measured monthly, the average concentration of pollutants (SO₂, NO and NO₂) and PM₁₀ (particles with a diameter less than ?10 μm) is used as input to forecast the monthly average concentration of PM₁₀ from one to 7 months ahead. Simulations showed that the ARIMA model performs better than the other models when forecasting 1 month ahead, while in the forecast from one to 9 months ahead the best performance is given by the support vector regression.     

Modeling the Effects of Meteorological Factors on SO2 and PM10 Concentrations with Statistical Approaches

Air quality has been deteriorated seriously in urban areas as a result of increasing anthropogenic activities. Meteorological conditions affect air pollution levels in the urban atmosphere significantly due to their important role in transport and dilution of the pollutants. This paper aims to investigate usability of some promising statistical methods for examining the impacts of metrological factors on SO₂ and PM10 levels. Data were collected from city centre of Kocaeli in winter periods from 2007 to 2010 as pollutant concentrations increase in winters due to expanding combustion facilities. Results of bivariate correlation analysis showed that humidity and rainfall have remarkable negative correlations with the pollutants. Multiple linear regression models and artificial neural network (ANN) models were used to predict next day's PM10 and SO₂ levels. In regression models calculated R² values were 0.89 and 0.75 for PM10 and SO₂, respectively. Among the various architectures, single layer networks provided better performance in ANN applications. Highest R² values were obtained as 0.89 and 0.69 for PM10 and SO₂, respectively, by using appropriate networks.     

Change detection using CUSUM and modified CUSUM method in air pollutant concentrations at traffic site in Delhi

Understanding the changes in the air pollution of an area due to implementation of control strategies is important as it helps in making further action plans. Time series analysis provides ways to interpret the effect of any policy changes. In this study, the applicability of the CUSUM method for change detection in air pollutant concentrations in Delhi is investigated. The method detects any shift from mean of the process. Delhi has undergone major policy changes during the past few years. Change of fuel in vehicles to compressed natural gas (CNG) is one amongst them. The data observed at a traffic site in Delhi for nitrogen dioxide (NO₂), carbon monoxide (CO) and particulate matter (with size less than 10 micron-PM10) concentrations is used to carry out the analysis. Increase in NO₂ concentration and decrease in CO concentration levels is observed using CUSUM method. The choice of base period does not affect much for these two pollutants but for PM10 concentration, however its role is crucial. In order to counter any variability shifts, the CUSUM method is further modified to account for the change in the variance of the time series. Modified CUSUM method indicated similar nature of variability in NO₂ and PM10, whereas CO variability has decreased significantly after CNG implementation.     

A geostatistical approach for assessing population exposure to NO2 in a complex urban area (Beirut,Lebanon)

Few studies in the Middle East region estimated the spatial distribution of air pollutants for exposure studies. This paper presents a geostatistical approach to assess background NO₂ spatial distribution and the associated exposed population in a Mediterranean city with a complex topography, Beirut. Such modeling gave an accurate mapping of the 2010 yearly background average value of NO₂: it varies between 35 and 67 μg m^?3 with a mean of 53 μg m^?3. The mean SD of the estimated error was about 3 μg m^?3. The results showed that the spatial distribution of NO₂ follows a nested structuring, with a major structure related to topoclimatic characteristics (interaction topography/atmospheric flow at large scale) and a minor one linked to micro-environment and micro-climatic characteristics (interactions urban morphology/atmospheric flows at fine scale). The probability for the city’s inhabitants to be exposed to NO₂ levels exceeding 40 μg m^?3 threshold limit set by the World Health Organization (WHO) showed that Beirut city has a real sanitary risk to the NO₂ pollution. 93 % of the population (around 358,459 people) is 100 % sure to be exposed to a yearly average exceeding 40 μg m^?3. This knowledge will be certainly useful for developing a tool for decision support in order to implement policies of reducing air pollution in Beirut, which is, given the results, very urgent.     

Assessment of the Kraków inhabitants’ health risk caused by the exposure to inhalation of outdoor air contaminants

Health risk associated with the exposure to the polluted atmospheric air inhalation was estimated for the residents of Kraków, Poland. The air pollution concentration data were obtained from the air-quality monitoring system of the city in 2007–2016. The carcinogenic risk of the studied subpopulations was not acceptable under the formula of C₆H₆ > BaP > As(PM10) > Cd(PM10) > Pb(PM10) > Ni(PM10). The total carcinogenic risk (Rt) amounted to 3.04E?04 for children, 2.22E?04 for infants, 1.45E?04 for women, and 1.22E?04 for men. The same risk was calculated for the top three locations of the monitoring stations in this respect, within the city of Kraków: Kurdwanów Housing Estate, Nowa Huta district, and Krasińskiego Av. Non-carcinogenic risk in the case of all six monitoring stations and in respect of all the studied subpopulations, resulting from the exposure to PM10 and for NO₂ for all stations in case of children and infants, as well as, for adults at Krasińskiego Av. and Dietla Str. stations was rated medium. For C₆H₆ in the case of adults, children, and infants the risk was rated low. The total risk (HI) of non-carcinogenic pollution was rated medium and ranged as follows: 6.53 for children, 4.70 for infants, 3.19 for women, and 2.67 for men. That type of risk was decreasing at the station locations as follows: Krasińskiego Av. > Dietla Str. > Nowa Huta district > Kurdwanów Housing Estate > Z?oty Róg Str. > Piastów Housing Estate.     

An empirical analysis of the impact of a bus rapid transit system on the concentration of secondary pollutants in the roadside areas of the TransJakarta corridors

A bus rapid transit (BRT) system began operation in Jakarta City, Indonesia, in January 2004 and led to a modal shift from private to public modes of transport. This modal shift from car and motorcycle to BRT reduced the emission intensity of primary pollutants, such as NO_x and CO. We applied a combined structural equation model and an artificial neural network to evaluate the impact of the BRT system on the concentration of secondary pollutants in the roadside areas in the BRT corridors. An empirical analysis was carried out using data collected at five continuous ambient air quality monitoring stations located near to the BRT TransJakarta corridors in 2005. The establishment of our structural equation model gives a better understanding of the cause–effect relationship among the factors influencing roadside ambient air pollution, and was useful in simplifying the complexity of our artificial neural network model for predicting the modal shift’s impact on the PM₁₀ values and concentration of O₃. The introduction of the BRT system, and the modal shift it produced, had a greater influence on rapidly decaying pollutants, such as PM₁₀, than on O₃ because of the exposure to near-source microenvironments, such as the roadside of the TransJakarta corridors.     

ARIMA forecasting of ambient air pollutants (O3, NO, NO2 and CO)

In the present study, a stationary stochastic ARMA/ARIMA [Autoregressive Moving (Integrated) Average] modelling approach has been adapted to forecast daily mean ambient air pollutants (O₃, CO, NO and NO₂) concentration at an urban traffic site (ITO) of Delhi, India. Suitable variance stabilizing transformation has been applied to each time series in order to make them covariance stationary in a consistent way. A combination of different information-criterions, namely, AIC (Akaike Information Criterion), HIC (Hannon–Quinn Information Criterion), BIC (Bayesian Information criterion), and FPE (Final Prediction Error) in addition to ACF (autocorrelation function) and PACF (partial autocorrelation function) inspection, has been tried out to obtain suitable orders of autoregressive (p) and moving average (q) parameters for the ARMA(p,q)/ARIMA(p,d,q) models. Forecasting performance of the selected ARMA(p,q)/ARIMA(p,d,q) models has been evaluated on the basis of MAPE (mean absolute percentage error), MAE (mean absolute error) and RMSE (root mean square error) indicators. For 20 out of sample forecasts, one step (i.e., one day) ahead MAPE for CO, NO₂, NO and O₃, have been found to be 13.6, 12.1, 21.8 and 24.1%, respectively. Given the stochastic nature of air pollutants data and in the light of earlier reported studies regarding air pollutants forecasts, the forecasting performance of the present approach is satisfactory and the suggested forecasting procedure can be effectively utilized for short term air quality forewarning purposes.     

Lockdown as an Empirical Strategy to Curb Air Pollution: Evidence from a Quasi-Natural Experiment

In this study, three approaches namely parallel, sequential, and multiple linear regression are applied to analyze the local air quality improvements during the COVID-19 lockdowns. In the present work, the authors have analyzed the monitoring data of the following primary air pollutants: particulate matter (PM₁₀ and PM_2.5), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and carbon monoxide (CO). During the lockdown period, the first phase has most noticeable impact on airquality evidenced by the parallel approach, and it has reflected a significant reduction in concentration levels of PM₁₀ (27%), PM_2.5 (19%), NO₂ (74%), SO₂ (36%), and CO (47%), respectively. In the sequential approach, a reduction in pollution levels is also observed for different pollutants, however, these results are biased due to rainfall in that period. In the multiple linear regression approach, the concentrations of primary air pollutants are selected, and set as target variables to predict their expected values during the city's lockdown period.The obtained results suggest that if a 21-days lockdown is implemented, then a reduction of 42 µg m⁻³ in PM₁₀, 23 µg m⁻³ in PM_2.5, 14 µg m⁻³ in NO₂, 2 µg m⁻³ in SO₂, and 0.7 mg m⁻³ in CO can be achieved.     

Evaluation and potential improvements of WRF/CMAQ in simulating multi-levels air pollution in megacity Shanghai,China

The accuracy of atmospheric numerical model is important for the prediction of urban air pollution. This study investigated and quantified the uncertainties of meteorological and air quality model during multi-levels air pollution periods. We simulated the air quality of megacity Shanghai, China with WRF/CMAQ (Weather Research and Forecasting model and Community Multiscale Air Quality model) at both non-pollution and heavy-pollution episodes in 2012. The weather prediction model failed to reproduce the surface temperature and wind speed in condition of high aerosol loading. The accuracy of the air quality model showed a clear dropping tendency from good air quality conditions to heavily polluted episodes. The absolute model bias increased significantly from light air pollution to heavy air pollution for SO₂ (from 2 to 14%) and for PM₁₀ (from 1 to 33%) in both urban and suburban sites, for CO in urban sites (from 8 to 48%) and for NO₂ in suburban sites (from 1 to 58%). A test of applying the Urban Canopy Model scheme to the WRF model showed fairly good improvement on predicting the meteorology field, but less significant effect on the air pollutants (6% for SO₂ and 19% for NO₂ decease in model bias found only in urban sites). This study gave clear evidence to the sensitivities of the model performance on the air pollution levels. It is suggested to consider this impact as a source for model bias in the model assessment and make improvement in the model development in the future.     

Monitoring and forecasting nitrate concentration in the groundwater using statistical process control and time series analysis: a case study

Contaminated water resources have important implications on health and the environment. Nitrate contamination of the groundwater is a serious problem in the European Union. A method based on the statistical process control (SPC) and time series analysis is developed to monitoring and to predict the concentration evolution of nitrate (NO₃ ⁻) in groundwater. In many pumping wells the NO₃ ⁻concentration ([NO₃ ⁻]) increases and approaches or even passes the European Community standard of 50 mg l⁻¹. The objective of this paper is to show the application of statistical process control as a monitoring tool for groundwater pollution from agricultural practices. We propose the autoregressive integrated moving average (ARIMA) model as a management tool to monitoring and reduction of the intrusion of nitrate into the groundwater. This tool should help in setting up useful guidelines for evaluating actual environmental performance against the firm’s environmental objectives and targets and regulatory requirements. We concluded that the statistical process control method may be a potentially important way of monitoring groundwater quality that also permits rapid response to serious increases in pollutants concentrations. In doing so, the paper fills an important gap in the water pollution standards and emerging polices (Water Framework directives).     

Irregularity analysis of CO,NO2 and O3 concentrations at traffic,commercial and low activity sites in Delhi

The irregularity analysis of exceedance time series of gaseous pollutants CO, NO₂ and O₃ is carried out using Shannon entropy and Fisher information measure. The data observed during 2007–2010 at three sites with different land-use activities in Delhi are analyzed. CO and NO₂ showed irregular behavior at both, low anthropogenic activity and commercial activity sites, whereas at traffic site both the pollutant concentrations showed regular behavior. The irregularity is attributed to the multiplicity in emission sources at low activity and commercial site and regular behavior is observed due to the uniformity and well defined source characteristics at the traffic site. O₃ at three sites showed irregular behavior owing to its secondary nature. Fisher–Shannon information plane showed the grouping of three pollutants except CO and NO₂ at traffic and O₃ at low activity site suggesting the similar temporal characteristics of the pollutants even at the sites with different land-use activities.     

Estimating influences of urbanizations on meteorology and air quality of a Central Business District in Shanghai, China

Two sensitivity simulations were performed and compared by model in order to understand how high-rise buildings influence meteorology and air quality in the Lujiazui Central Business District (CBD) of Shanghai, China. The coupled meteorological-photochemical model, Metphomod, was used, with a 500-m horizontal resolution and the observations and the simulated results generally agreed well. The scheme considering buildings within roughness could reduce uncertainties in the simulated meteorological conditions and concentrations of air pollutants. The high-rise buildings decreased wind speeds by 0.5–4 m/s, increased temperatures by up to 1 °C and turbulent kinetic energy by 1–2 J/m³ in the Lujiazui CBD. The changes in meteorological conditions also increased NO by about 2–5 %. However, the complex meteorological changes of higher temperatures and stronger turbulent kinetic energy, coupled with changes of precursors’ concentrations in the Lujiazui CBD, decreased O₃ concentrations by up to 6 % somewhere, while increasing O₃ formation by up to 2 % in downwind areas. The results suggested that it was necessary to include high-rise building parameters in models when estimating the meteorology and diagnosing air pollution of highly urbanized regions.     

Mechanism for the formation of the January 2013 heavy haze pollution episode over central and eastern China

In January 2013,a long-lasting episode of severe haze occurred in central and eastern China,and it attracted attention from all sectors of society.The process and evolution of haze pollution episodes were observed by the"Forming Mechanism and Control Strategies of Haze in China"group using an intensive aerosol and trace gases campaign that simultaneously obtained data at 11 ground-based observing sites in the CARE-China network.The characteristics and formation mechanism of haze pollution episodes were discussed.Five haze pollution episodes were identified in the Beijing-Tianjin-Hebei(Jing-Jin-Ji)area;the two most severe episodes occurred during 9–15 January and 25–31 January.During these two haze pollution episodes,the maximum hourly PM2.5mass concentrations in Beijing were 680 and 530μg m 3,respectively.The process and evolution of haze pollution episodes in other major cities in the Jing-Jin-Ji area,such as Shijiazhuang and Tianjin were almost the same as those observed in Beijing.The external cause of the severe haze episodes was the unusual atmospheric circulation,the depression of strong cold air activities and the very unfavorable dispersion due to geographical and meteorological conditions.However,the internal cause was the quick secondary transformation of primary gaseous pollutants to secondary aerosols,which contributed to the"explosive growth"and"sustained growth"of PM2.5.Particularly,the abnormally high amount of nitric oxide(NOx)in the haze episodes,produced by fossil fuel combustion and vehicle emissions,played a direct or indirect role in the quick secondary transformation of coal-burning sulphur dioxide(SO2)to sulphate aerosols.Furthermore,gaseous pollutants were transformed into secondary aerosols through heterogeneous reactions on the surface of fine particles,which can change the particle’s size and chemical composition.Consequently,the proportion of secondary inorganic ions,such as sulphate and nitrate,gradually increased,which enhances particle hygroscopicity and thereby accelerating formation of the haze pollution.     

Analysis of Summer Ozone Observations at a High Mountain Site in Central Italy (Campo Imperatore, 2388 m a.s.l.)

Tropospheric ozone (O₃) is an important atmospheric pollutant and climate forcer. The Mediterranean basin is a hot-spot region in terms of short-term O₃ distribution, with frequent episodes of high tropospheric O₃, especially during summer. To improve the characterisation of summer O₃ variability in the Mediterranean area, during the period 6–27 August 2009 an experimental campaign was conducted at Campo Imperatore, Mt Portella (CMP), a high mountain site (2,388 m a.s.l.) located in the central Italian Apennines. As deduced from analysis of atmospheric circulation, the measurement site was significantly affected by air masses originating over the Mediterranean basin, which affected the measurement site for 32 % of the time. Analysis of average values and diurnal and day-to-day variability revealed that CMP O₃ observations (average value 60.0 ± 5.1 ppbv) were comparable with measurements at other European mountain stations, indicating a prevalent effect of meteorological conditions and atmospheric transport on the synoptic scale. In fact, only a small “reverse” diurnal variation typically characterises diurnal O₃ variability because of local thermal wind circulation, which sporadically favours transport of air masses rich in O₃ from the foothill regions. Statistical analysis of five-day back-trajectory ensembles indicates that synoptic-scale air-mass transport from the Mediterranean Sea usually results in decreasing O₃ concentrations at CMP, whereas the highest hourly O₃ values are mostly associated with air masses from central continental Europe, eastern Europe, and northern Italy. High O₃ concentrations are also related to downward air-mass transport from higher altitudes. Comparison of in-situ O₃ variability with tropospheric O₃ satellite-based measurements reveals similar features of the two data sets. Together with the results from back-trajectory analysis, this indicates that CMP measurements might usefully improve characterisation of broad-scale O₃ variability over the central Mediterranean basin.     

三峡大坝上下游水质时空变化特征

为探索三峡大坝上下游（坝上99.9 km、坝下63.0 km、全长162.9 km）水质时空变化特征,运用主成分分析和方差分析对2016年近坝段水质时空变化特征进行了分析.主成分分析表明,水文因子流量（Q）、气温（T）、水位（Z）和水质因子（水温（WT）、pH、电导率（EC）、溶解氧（DO）、悬浮物（SS）、高锰酸盐指数（COD_Mn）、硫酸盐（SO₄^2-）、氟化物（F^-）、总硬度（T-Hard）、硝态氮（NO₃^--N）、总氮（TN）和硒（Se））的变化主导着研究区域水质变化;各采样点主成分得分和双因素方差分析结果显示研究区域水质因子时间变化主要呈现出季节和不同水库运行时期的差异.消落期（2-5月）,T-Hard、F^-、SO₄^2-和EC是影响河流水质变化的主导因子;汛期（7-8月）,Q、SS、COD_Mn、NO₃^--N、TN和Se是影响河流水质变化的主导因子;T和WT主导着汛末（9月）河流水质变化,并引起了DO等理化特性的变化;高水位运行期（12月）,Cl^-是影响河流水质变化的主导因子.现阶段,DO、有机污染物（COD_Mn）、无机盐（SO₄^2-和F^-）、营养盐类（NO₃^--N和TN）、类金属元素（Se）和水体的矿化程度（T-Hard）的变化主导着区域水质的变化,是三峡大坝近坝段水域水质的控制因子.方差分析表明,河流的理化特性（DO、pH和SS）、营养盐组分构成（NH₃-N和NO₃^--N）、无机盐类（EC和Cl^-）、石油类有机污染物及粪大肠菌群（FC）等指标在坝上与坝下断面存在显著性差异.气温、水温、降雨、含沙量的季节性影响因素和水库调度运行模式是影响近坝段水质时间差异的主要因子;空间差异主要受城区污染排放和三峡水库调度引起的坝上和坝下水文和水动力学条件差异影响.因此控制研究区域因人类活动等造成的外源性污染,并针对不同类污染物质的季节变化特征实施合理的水库运行方式是近坝段水质提升的关键.     

Role of Nitrogen Oxides,Black Carbon,and Meteorological Parameters on the Variation of Surface Ozone Levels at a Tropical Urban Site – Hyderabad,India

In this study, temporal variations of surface ozone (O₃) were investigated at tropical urban site of Hyderabad during the year 2009. O₃, oxides of nitrogen (NO_x = NO + NO₂), black carbon (BC), and meteorological parameters were continuously monitored at the established air monitoring station. Results revealed the production of surface O₃ from NO₂ through photochemical oxidation. Averaged datasets illustrated the variations in ground‐level concentrations of these air pollutants along different time scales. Maximum mean concentrations of O₃ (56.75 ppbv) and NO_x (8.9 ppbv) were observed in summer. Diurnal‐seasonal changes in surface O₃ and NO_x concentrations were explicated with complex atmospheric chemistry, boundary layer dynamics, and local meteorology. In addition, nocturnal chemistry of NO_x played a decisive role in the formation of O₃ during day time. Mean BC mass concentration in winter (10.92 µg m^?3) was high during morning hours. Heterogeneous chemistry of BC on O₃ destruction and NO_x formation was elucidated. Apart from these local observations, long‐range transport of trace gases and BC aerosols were evidenced from air mass back trajectories. Further, statistical modeling was performed to predict O₃ using multi‐linear regression method, which resulted in 91% of the overall variance.     

Zum Auftreten von leichtflüchtigen Halogenkohlenwasserstoffen im Badewasser

110 samples from two swimming-pools were gas-chromatographically investigated for halogenated hydrocarbons. In spite of varying chlorine concentrations between 0.1 and 2.5 mg/l Cl₂ the bacteriological results were unobjectionable. The total of the haloforms was between 1.2 and 182 μg/l, with the average value of 90 μg/l. Besides CHCl₃, CHBrCl₂, CHBr₂Cl and CCl₃NO₂ there were observed other, still unidentified compounds in the chlorinated bathing water which clearly are successive products of disinfection, too. The water treatment by flocculation and filtration does not bring about any elimination of trihalomethanes. Of special importance is the occurrence of trichloronitromethane in concentrations of 0.13 … 1.2 μg/l, whose LC₅₀ in the Daphnia test is 189 μg/l, at a threshold concentration of 160 μg/l. To limit the pollution of water and air to a minimum, specific conditions must be adhered to in the operation of indoor swimming-pools.     

Compositional time series analysis for Air Pollution Index data

The increasing importance of understanding the structure of Air Pollution Index (API) makes it necessary to come out with a compositional of API based on its pollutants. This will be more comprehensible for the public and easier to cooperate with authorities in reducing the causes of air pollution. Since five pollutants contribute in determining the API values, API can be shown as a compositional data. This study is conducted based on the data of API and its pollutants collected from Klang city in Malaysia for the period of January 2005 to December 2014. The proportion of each pollutant in API is considered as a component with five components in a compositional API. The existence of zero components in some pollutants, that have no effect on API, is a serious problem that prevents the application of log-ratio transformation. Thus, the approach of amalgamation has been used to combine the components with zero in order to reduce the number of zeros. Also, a multiplicative replacement has been utilized to eliminate the zero components and replace them with a small value that maintains the ratios of nonzero components. Transforming the compositional data to log-ratio coordinates has been done using the additive log ratio transformation, and the transformed series is then modeled by using a VAR model. Four criteria are used to determine the number of lags p of VAR(p) and these are: the Akaike Information, the Schwartz, the Hannan–Quinn and the Final Prediction Error criteria. Based on the results, A VAR (1) model with no constants or trend is considered as the best fitted model and it is used to forecast 12 months ahead. In addition, API values are mainly determined by PM₁₀ that has a proportion close to one most of the time during study period. Therefore, authorities and researchers need to study the sources of PM₁₀ and provide the public with useful information and alternatives in term of reducing the air pollution.     

Geographies of uncertainty in the health benefits of air quality improvements

Assessing the long-term benefits of marginal improvements in air quality from regulatory intervention is methodologically challenging. In this study, we explore how the relative risks (RRs) of mortality from air pollution exposure change over time and whether patterns in the RRs can be attributed to air quality improvements. We employed two-stage multilevel Cox models to describe the association between air pollution and mortality for 51 cities with data from the American Cancer Society (ACS) cohort (N = 264,299, deaths = 69,819). New pollution data were computed through models that predict yearly average fine particle (PM_2.5) concentrations throughout the follow-up (1982–2000). Average PM_2.5 concentrations from 1999 to 2000 and sulfate concentrations from 1980 were also examined. We estimated the RRs of mortality associated with air pollution separately for five time periods (1982–1986, 1987–1990, 1991–1994, 1995–1998, and 1999–2000). Mobility models were implemented with a sub-sample of 100,557 subjects to assist with interpreting the RR estimates. Sulfate RRs exhibit a large decline from the 1980s to the 1990s. In contrast, PM_2.5 RRs follow the opposite pattern, with larger RRs later in the 1990s. The reduction in sulfate RR may have resulted from air quality improvements that occurred through the 1980s and 1990s in response to the acid rain control program. PM_2.5 concentrations also declined in many places, but toxic mobile sources are now the largest contributors to PM in urban areas. This may account for the heightened RR of mortality associated with PM_2.5 in the 1990s. The paper concludes with a three alternative explanations for the temporal pattern of RRs, each emphasizing the uncertainty in ascribing health benefits to air quality improvements.