Assessment of regional metal levels in ambient air by statistical regression models

The assessment of the particulate matter (PM) levels and its constituents presented in the atmosphere is an important requirement of the air quality management and air pollution abatement. The heavy metal levels in PM10 are commonly evaluated by experimental measurements; nevertheless, the EC Directives also allow the Regional Governments to estimate the regulated metal levels (Pb in Directive 2008/50/EC and As, Ni and Cd in Directive 2004/107/EC) by objective estimation and modelling techniques. These techniques are proper alternatives to the experimental determination because the required analysis and/or the number of required sampling sites are reduced. The present work aims to estimate the annual levels of regulated heavy metals by means of multivariate linear regression (MLR) and principal component regression (PCR) at four sites in the Cantabria region (Northern Spain). Since the objective estimation techniques may only be applied when the regulated metal concentrations are below to the lower assessment threshold, a previous evaluation of the determined annual levels of heavy metals is conducted to test the fulfilment of the EC Directives requirements. At the four studied sites, the results show that the objective estimations are allowed alternatives to the experimental determination. The annual average metal concentrations are well estimated by the MLR technique in all the studied sites; furthermore, the EC quality requirements for the objective estimations are fulfilled by the developed statistical MLR models. Hence these estimations may be used by Regional Governments as a proper alternative to the experimental measurements for the regulated metal levels assessment.     

Evaluation of environmental impacts of Integrated Industrial Estate��Pantnagar through application of air and water quality indices

Large-scale industrialization, population inflow, and rapid urbanization coupled with unfavorable meteorological conditions often induce significant degradation of urban environment. In order to assess the extent of environmental impacts due to establishment of the Integrated Industrial Estate??Pantnagar (IIE-Pantnagar), ambient air and groundwater were monitored from June 2007 to May 2008. Collected baseline information was normalized and interpreted with the application of air (AQI) and water quality indices (WQI). Among the pre-identified air pollutants, suspended particulate matter was found to be the principal culprit to deteriorate ambient air quality, with a maximum annual concentration of 418.5 ??g/m³. Monthly average concentrations of respirable particulate matter (aerodynamic diameter < 10 ??m) also persist at a critical level with an annual maximum of 207.3 ??g/m³. A segmented linear function with maximum operator concept was used to compute AQI, and the developed index was found well suitable to demonstrate temporal variations of ambient air quality. The computed AQI value for the selected study region varied from moderate (97.0) to very poor pollution level (309.2) in respect to developed air quality standards. Furthermore, an integrated WQI was developed comprising 9 parameters, and among all the 10 pre-identified locations, the average groundwater quality was found acceptable in terms of Indian drinking water standards. The maximum WQI (70.6) was found at the Kichha Railway Station during summer months, revealing moderate pollution load. Industrial discharge from IIE-Pantnagar coupled with other industrial setup may hold responsible for such kind of degradation of water quality. In contrast, WQI computed at Rudrapur City demonstrate minimum (15.0?C22.1) pollution load. For 95% of the monitoring period, the computed WQI was found acceptable for all selected locations with few exceptions. The application of WQI to assess temporal variations in groundwater quality was therefore found satisfactory.     

Statistical Analysis of Air Pollutants and Meteorological Parameters in Afyon, Turkey

In this study, the relation between sulfur dioxide (SO₂) and particulate matter (PM) concentrations periodically measured in the city of Afyon’s atmosphere with meteorological factors such as precipitation, humidity, temperature, wind velocity, and inversion were investigated. The mean values of SO₂ and PM concentrations measured during the winter months of October–March 1990–1999 were correlated with the meteorological parameters of the same period. Simple and multiple linear regression analysis were utilized to evaluate the contribution of meteorological variables. The statistical results show that the pollutants, i.e., SO₂ and PM are dependent upon humidity, temperature, and inversion at the 1% significance level; while the dependence of both pollutants with temperature is negative when those of humidity and inversion are positive. Two models in which temperature and inversion are dependent with multiple variables are recommended for predicting the contribution of meteorological parameters on SO₂ and PM. In addition, the relationship between humidity, temperature, and inversion with pollutants is also determined using nonlinear (polynomial) models.     

Seasonal evaluation and spatial variability of suspended particulate matter in the vicinity of a large coal-fired power station in India—A case study

Coal combustion in the power sector gives rise to the emission of primary and secondary particulate pollutants. Since the emission of pollutants depends on coal quality and combustion technology, and given that transport, transformation and deposition of contaminants depend on regional climatic conditions, specific studies for the power stations is needed to evaluate their environmental impacts. Monitoring of ambient respirable suspended particulate matter (RSPM) and suspended particulate matter (SPM) levels around a large coal-fired power station in India was carried out. The specific objectives were the determination of spatial and seasonal variability in RSPM and SPM levels, and their relationship with meteorological parameters such as wind velocity and relative humidity. The results have shown a marked seasonal trend and spatial variability in RSPM and SPM levels in the study area. Higher concentrations of ambient RSPM and SPM were found in downwind monitoring stations compared to upwind direction. Ratios of RSPM to SPM and correlation coefficient values between RSPM and SPM along with meteorological parameters were also worked out. Relative humidity and wind velocity have shown an inverse relation with particulate deposition pattern.     

Global air quality monitoring: Trends and levels of sulphur dioxide and suspended particulate matter in selected urban areas

An overview is presented of the development and ongoing activities of the WHO/UNEP air quality monitoring project. The project started in 1973 and is part of the Global Environmental Monitoring System. An analysis of the sulphur dioxide and suspended particulate matter is also included. The results show that the annual mean concentration for sulphur dioxide has decreased since 1973; for suspended particulate matter, however, a gradual increase is observed since 1976.     

Influence of aerosol spectrum and air pollutants on fog formation in urban environment of megacity Delhi, India

Urban areas are facing increasing fog frequencies that may result due to increased air pollution emanating from variety of sources. The increased pollution levels may lead to the atmospheric reactions resulting into the formation of secondary pollutants that may also lead to increased aerosol number concentrations (ANC) in the atmosphere. This could cause enhanced water aerosols in the presence of favourable meteorological conditions and high relative humidity. This study deals with the atmospheric pollution and visibility during winter season of megacity Delhi in order to assess the relationship between the two specifically during fog episodes. Thus, this study analyses the levels of air pollutants, aerosol spectrum and meteorological conditions during one week each in the winter season of the years 2004 and 2006 in order to have an improved understanding of their role in fog formation in mega-city Delhi. More than 300 h of measurements which included episodes of dense, thick and moderate fogs of about 25 h, were studied. The measurements cover most of the accumulation mode and greater size spectrum of aerosols. Thus, the analysis is performed for the entire period, specifically, before the fog sets up, during and afterwards. In general, the relatively small variations in number concentration show larger variations in visibility prior and post dense fog formation than during dense fog episodes. Preliminary analysis of monthly averaged RSPM (Respirable Suspended particulate Matter or PM(10)) concentration values for four winter months for a period of 6 years (1996-2001) and visibility did not show a good correlation with total occurrences of fog. However, daily averaged RSPM concentration showed a good correlation with the occurrences of thick fog. Diurnal variation of Sulfur-dioxide and Nitrogen dioxide were found to have inverse relationship with visibility during fog which may be due to formation of secondary pollutants such as sulfate and to a lesser extent nitrates. Amongst, the daily averaged concentrations of all the criteria pollutants, RSPM was found to be best correlated with the fog in comparison to other pollutants.     

湖南省大气污染特征及气象影响分析

为深入探究湖南省大气污染过程变化特征及气象条件对不同首要污染物的影响,利用2015—2021年湖南省14个市州的大气环境监测数据及气象观测数据,分析研究了68次污染过程的持续时长、分布特征等,并根据污染来源分析结果、传输路径分析结果,结合地形特征,进行了污染影响区域划分,对不同首要污染物的气象因子反馈机制进行了细致剖析。结果表明:湖南省大气污染过程以颗粒物污染为主,年变化特征呈现为单峰形,单次污染过程持续时长一般不超过3 d;3条主要的污染物传播路径均是由湘北地区进入长株潭城市群;污染来源以本地积累为主,外来输送与本地污染叠加次之;颗粒物污染主要受边界层稳定程度、逆温情况和近地面空气湿度的影响,臭氧污染则主要受紫外辐射、前体物浓度和扩散条件等的影响。     

Influence of Meteorological Factors NO2, SO2, CO and PM10 on the Concentration of O3 in the Urban Atmosphere of Eastern Croatia

Ozone, NO₂, SO₂, CO, PM₁₀ and meteorological parameters were measured simultaneously during the summer?Cautumn season 2007 in Osijek??the eastern, flat, agricultural part of Croatia. Fourier analysis confirms the existence of variation in ozone volume fractions with periods ranging from the usual semi-daily and daily to 7 and 28 daily cycles. The relationships between O₃ and other variables were modelled in three ways: principal component analysis, multiple linear regression and principal component regression. The results of the principal component analysis detected underlying relationships among ozone concentrations and meteorological variables. An extremely simple meteorological model is suitable for the prediction of ozone levels. The meteorological factors, temperature and cloudiness played a main role in the MLR model (R ²?=?0.83). The application of the principal component regression approach confirmed that the original variables associated with the valid principal components were meteorological variables (R ²?=?0.82).     

Short-Term Forecasting of Nitrogen Dioxide (NO<Subscript>2</Subscript>) Levels Using a Hybrid Statistical and Air Mass History Modelling Approach

A novel hybrid model has been developed to support the provision of real-time air quality forecasts. Statistical techniques have been applied in parallel with air mass history modelling to provide an efficient and accurate forecasting system with the ability to identify high NO₂ events, which tend to be the episodes of most significance in Ireland. Air mass history modelling and k-means clustering are used to identify air mass types that lead to high NO₂ levels in Ireland. Trajectory matching techniques allow data associated with these air masses to be partitioned during model development. Non-parametric regression (NPR) has been applied to describe nonlinear variations in concentration levels with wind speed, direction and season and produce a set of linearized factors which, together with other meteorological variables, are employed as inputs to a multiple linear regression. The model uses an innovative integrated approach to combine the NPR with the air mass history modelling results. On validation, a correlation coefficient of 0.75 was obtained, and 91 % of daily maximum (hourly averaged) NO₂ predictions were within a factor of two of the measured value. High pollution events were well captured, as indicated by strong agreement between measured and modelled high percentile values. The model requires only simple input data, does not require an emission inventory and utilises very low computational resources. It represents an accurate and efficient means of producing real-time air quality forecasts and, when used in combination with forecaster experience, is a useful tool for identifying periods of poor air quality 24 h in advance. The hybrid approach outlined in this paper can easily be applied to produce high-quality forecasts of both NO₂ and additional pollutants at new locations/countries where historical monitoring data are available.     

Particulate Matter Over A Seven Year Period in Urban and Rural Areas Within, Proximal and Far from Mining and Power Station Operations in Greece

Lignite mining operations and lignite-fired power stations result in major particulate pollution (fly ash and fugitive dust) problems in the areas surrounding these activities. The problem is more complicated, especially, for urban areas located not far from these activities, due to additional contribution from the urban pollution sources. Knowledge of the distribution of airborne particulate matter into size fraction has become an increasing area of focus when examining the effects of particulate pollution. On the other hand, airborne particle concentration measurements are useful in order to assess the air pollution levels based on national and international air quality standards. These measurements are also necessary for developing air pollutants control strategies or for evaluating the effectiveness of these strategies, especially, for long periods. In this study an attempt is made in order to investigate the particle size distribution of fly ash and fugitive dust in a heavy industrialized (mining and power stations operations) area with complex terrain in the northwestern part of Greece. Parallel total suspended particulates (TSP) and particulate matter with an aerodynamic diameter less than 10 μm (PM10) concentrations are analyzed. These measurements gathered from thirteen monitoring stations located in the greater area of interest. Spatial, temporal variation and trend are analyzed over the last seven years. Furthermore, the geographical variation of PM10 – TSP correlation and PM10/TSP ratio are investigated and compared to those in the literature. The analysis has indicated that a complex system of sources and meteorological conditions modulate the particulate pollution of the examined area.     

The Seasonal Variation of the Concentrations of Ozone, Sulfur Dioxide, and Nitrogen Oxides in Two Nigerian Cities

A combination of multivariate statistical methods including factor analysis, principal component analysis, principal component regression, and multiple linear regression (MLR) were employed to evaluate the influence of seasons on the concentrations of ozone, sulfur (IV) oxide, and oxides of nitrogen in ambient air of Nigerian cities of Lagos and Ilorin. The former city is located in the coastal area, and it is highly congested with a high intensity of marine, vehicular, and industrial activities, and the latter city is a medium size town, located in the central guinea savannah zone of Nigeria. Samples were collected using a high-volume sampler from near the ground at various sites of diverse human and industrial activities, during wet and dry seasons from 2003 to 2006. The PCA reveals three distinct groupings during the day for all data, which is a reflection of different factors contributing to the atmospheric chemistry of these cities. The predicted ozone concentration values by MLR agree fairly well with the measured data. The dependence of ozone on meteorological parameters including relative humidity, air temperature, and sun exposure and the precursor pollutants depends on weather and the anthropogenic activities. The results for the two cities indicate that reduction in the level of NO₂ is accompanied by an increase in the level of ozone, suggesting the interconversion between the two via photochemical activity.     

Simulation of PM<Subscript>10</Subscript> concentration patterns for a 2010 traffic scenario in Bologna,Italy

Three state of the art traffic–emission–dispersion models dealing with particulate matter have been tested and validated over the Bologna metropolitan area with 2001 data and a future scenario has been developed in order to estimate expected PM concentrations in 2010. The modelling system is composed by a traffic model (VISUM) evaluating vehicle fluxes as a function of mobility demand and road network in the area, an emission model (Trefic) estimating pollutants emitted in atmosphere as a function of vehicle fluxes amount and composition and of environmental conditions and a dispersion model (ADMS) evaluating PM concentrations on the area, given the meteorological variables. The three models compose a cascade sequence and results of the previous one feed the next one. PM concentrations computed by the model suite for the town of Bologna, in northern Italy, for the reference period (January 2001) have been compared with air quality stations measurements suggesting the modelling system being especially suitable for evaluating traffic induced PM. Qualitative and quantitative changes in the circulating vehicle fleet have been supposed in order to obtain a realistic scenario for year 2010. Forecasted concentrations have been then compared with limits fixed by current EU legislation for particulate matter.     

Influence of Meteorological Parameters on Particulates and Atmospheric Pollutants at Taichung Harbor Sampling Site

Atmospheric aerosol particles and metallic concentrations, ionic species were monitored at the Experimental harbor of Taichung sampling site in this study. This work attempted to characterize metallic elements and ionic species associated with meteorological conditions variation on atmospheric particulate matter in TSP, PM_2.5, PM_2.5–10. The concentration distribution trend between TSP, PM_2.5, PM_2.5–10 particle concentration at the TH (Taichung harbor) sampling site were also displayed in this study. Besides, the meteorological conditions variation of metallic elements (Fe, Mg, Cr, Cu, Zn, Mn and Pb) and ions species (Cl⁻, NO₃ ⁻, SO₄ ²⁻, NH₄ ⁺, Mg²⁺, Ca²⁺ and Na⁺) concentrations attached with those particulate were also analyzed in this study. On non-parametric (Spearman) correlation analysis, the results indicated that the meteorological conditions have high correlation at largest particulate concentrations for TSP at TH sampling site in this study. In addition, the temperature and relative humidity of meteorological conditions that played a key role to affect particulate matter (PM) and have higher correlations then other meteorological conditions such as wind speed and atmospheric pressure. The parameter temperature and relative humidity also have high correlations with atmospheric pollutants compared with those of the other meteorological variables (wind speed, atmospheric pressure and prevalent wind direction). In addition, relative statistical equations between pollutants and meteorological variables were also characterized in this study.     

Estimation of air pollutant emission loads from construction and operational activities of a port and harbour in Mumbai, India

Port causes environmental and health concerns in coastal cities if its operation and development are not made environmentally compatible and sustainable. An emission inventory is necessary to assess the impact of port projects or growth in marine activity as well as to plan mitigation strategies. In this study, a detailed emission inventory of total suspended particulate (TSP) matter, respirable particulate matter (PM₁₀), sulphur dioxide (SO₂) and oxides of nitrogen (NO_x) for a port having operation and construction activities in parallel is compiled. The study has been done for 1 year. Results show that the maximum contribution of emission of air pollutants in the port area was from TSP (68.5%) and the minimum was from SO₂ (5.3%) to the total pollutants considered in this study. Total TSP emission from all activities of the port was 4,452 tyr^???1 and PM₁₀ emission was 903 tyr^???1 in the year 2006. Re-suspension of dust from paved roads was the major contributor of TSP and PM₁₀ in the road transport sector. Construction activities of the port had contributed 3.9% of TSP and 7.4% of PM₁₀ to total emission of particulate matter. Of the total particulate emissions from various port activities approximately 20% of TSP could be attributed to PM₁₀. The sectoral composition indicates that major contribution of SO₂ emission in the port was from maritime sector and major contribution of NO_x was from road transport sector.     

Mathematical models for accurate prediction of atmospheric visibility with particular reference to the seasonal and environmental patterns in Hong Kong

Atmospheric visibility impairment has gained increasing concern as it is associated with the existence of a number of aerosols as well as common air pollutants and produces unfavorable conditions for observation, dispersion, and transportation. This study analyzed the atmospheric visibility data measured in urban and suburban Hong Kong (two selected stations) with respect to time-matched mass concentrations of common air pollutants including nitrogen dioxide (NO(2)), nitrogen monoxide (NO), respirable suspended particulates (PM(10)), sulfur dioxide (SO(2)), carbon monoxide (CO), and meteorological parameters including air temperature, relative humidity, and wind speed. No significant difference in atmospheric visibility was reported between the two measurement locations (p > or = 0.6, t test); and good atmospheric visibility was observed more frequently in summer and autumn than in winter and spring (p < 0.01, t test). It was also found that atmospheric visibility increased with temperature but decreased with the concentrations of SO(2), CO, PM(10), NO, and NO(2). The results showed that atmospheric visibility was season dependent and would have significant correlations with temperature, the mass concentrations of PM(10) and NO(2), and the air pollution index API (correlation coefficients mid R: R mid R: > or = 0.7, p < or = 0.0001, t test). Mathematical expressions catering to the seasonal variations of atmospheric visibility were thus proposed. By comparison, the proposed visibility prediction models were more accurate than some existing regional models. In addition to improving visibility prediction accuracy, this study would be useful for understanding the context of low atmospheric visibility, exploring possible remedial measures, and evaluating the impact of air pollution and atmospheric visibility impairment in this region.     

A Statistical Analysis of Particulate Data Sets for Jawaharlal Nehru Port and Surrounding Harbour Region in India

The concentrations of total suspended particulate matter (TSP) and particulate matter less than 10 microns (PM10) were measured at various locations in a Jawaharlal Nehru port and surrounding harbour region. Meteorological data was also collected to establish the correlation with air pollutant concentration. The results are analysed from the standpoint of monthly and seasonal variations, annual trends as well as meteorological effects. The monthly mean concentration of TSP was in the range of 88.2 to 199.3 microg m(-3). The maximum and minimum-recorded value of PM10 was 135.8 and 20.3 microg m(-3), respectively. The annual average concentration of PM10 was 66.1 microg m(-3). There are clear associations between TSP and PM10 data set at all the measured three sites with a correlation coefficient of 0.89, 0.69 and 0.81, respectively. PM10 data appears to be a constant fraction of the TSP data throughout the year, indicating common influences of meteorology and sources. Particle size analysis showed PM10 to be 47% of the total TSP concentration, which is lower than reported for industrial area and traffic junctions in Mumbai. Anthropogenic sources contribute significantly to the PM10 fraction in an industrial region, while contributions from natural sources are more in a port and harbour area. Statistical analysis of air quality data shows that TSP is strongly correlated with wind speed but weakly correlated with temperature. There appears to be a simple inverse relationship between TSP and wind speed data, indicating the dilution and transport by winds.     

River discharge as a major driving force on spatial and temporal variations in zooplankton biomass and community structure in the Godavari estuary India

Variability in horizontal zooplankton biomass distribution was investigated over 13 months in the Godavari estuary, along with physical (river discharge, temperature, salinity), chemical (nutrients, particulate organic matter), biological (phytoplankton biomass), and geological (suspended matter) properties to examine the influencing factors on their spatial and temporal variabilities. The entire estuary was filled with freshwater during peak discharge period and salinity near zero, increased to ~ 34 psu during dry period with relatively high nutrient levels during former than the latter period. Due to low flushing time (< 1 day) and high suspended load (> 500 mg L^?1) during peak discharge period, picoplankton (cyanophyceae) contributed significantly to the phytoplankton biomass (Chl-a) whereas microplankton and nanoplankton (bacillariophyceae, and chlorophyceae) during moderate and mostly microplankton during dry period. Zooplankton biomass was the lowest during peak discharge period and increased during moderate followed by dry period. The zooplankton abundance was controlled by dead organic matter during peak discharge period, while both phytoplankton biomass and dead organic matter during moderate discharge and mostly phytoplankton biomass during dry period. This study suggests that significant modification of physico-chemical properties by river discharge led to changes in phytoplankton composition and dead organic matter concentrations that alters biomass, abundance, and composition of zooplankton in the Godavari estuary.     

Spatial and temporal variations of SPM, RPM, SO2 and NOx concentrations in an opencast coal mining area

A study for assessment and management of air quality was carried out in the Ib Valley area of the Ib Valley coalfield in Orissa state, India. The 24 h average concentrations of suspended particulate matter (SPM), respirable particulate matter (RPM), sulfur dioxide (SO(2)) and oxides of nitrogen (NO(x)) were determined at regular intervals throughout one year at twelve monitoring stations in residential areas and six monitoring stations in mining/industrial areas. The 24 h average SPM and RPM concentrations were 124.6-390.3 microg m(-3) and 25.9-119.9 microg m(-3) in residential areas, and were 146.3-845.2 microg m(-3) and 45.5-290.5 microg m(-3) in industrial areas. During the study period, 24 h and annual average SPM and RPM concentrations exceeded the respective standards set in the Indian national ambient air quality standard (NAAQS) protocol as well as USEPA, EU, WHO and World Bank standards at most of the residential and industrial areas. However, concentrations of SO(2)(annual average: 24.6-36.1 microg m(-3) and 24 h average: 17.0-46.3 microg m(-3)) and NO(x)(annual average: 23.6-40.9 microg m(-3) and 24 h average: 18.3-53.6 microg m(-3)) were well within the prescribed limit of the NAAQS and international standards in both residential and industrial areas. The temporal variations of SPM and RPM fitted polynomial trends well and on average in the mining area 31.91% of the SPM was RPM. The linear regression correlation coefficients between SPM and RPM and between NO(x) and SO(2) were 0.94 (+/-0.04) and 0.66 (+/-0.10), respectively. The optimum interpolation technique, kriging, determined that maximal concentrations of SPM and RPM occurred within the mining site. Highest concentrations of particulate matter were observed during the winter season followed by summer, autumn and rainy seasons. An action plan is formulated for effective control of air pollution at source, and mitigative measures should include implementation of green belts around the sensitive areas where the concentration of air pollutants exceeds the standard limit.     

Prediction models of CO, SPM and SO2 concentrations in the Campo de Gibraltar Region, Spain: a multiple comparison strategy

The 'Campo de Gibraltar' region is a very industrialized area where very few air pollution studies have been carried out. Up to date, no model has been developed in order to predict air pollutant levels in the different towns spread in the region. Carbon monoxide (CO), Sulphur dioxide (SO(2)) and suspended particulate matter (SPM) series have been investigated (years 1999-2000-2001). Multilayer perceptron models (MLPs) with backpropagation learning rule have been used. A resampling strategy with two-fold crossvalidation allowed the statistical comparison of the different models considered in this study. Artificial neural networks (ANN) models were compared with Persistence and ARIMA models and also with models based on standard Multiple Linear Regression (MLR) over test sets with data that had not been used in the training stage. The models based on ANNs showed better capability of generalization than those based on MLR. The designed procedure of random resampling permits an adequate and robust multiple comparison of the tested models. Principal component analysis (PCA) is used to reduce the dimensionality of data and to transform exogenous variables into significant and independent components. Short-term predictions were better than medium-term predictions in the case of CO and SO(2) series. Conversely, medium-term predictions were better in the case of SPM concentrations. The predictions are significantly promising (e.g., d (SPM 24-ahead) = 0.906, d (CO 1-ahead) = 0.891, d (SO2 1-ahead) = 0.851).     

Evaluation of relationship between meteorological parameters and air pollutant concentrations during winter season in Elazığ, Turkey

In recent years, due to the rapid increase in population density, building density and energy consumption, the outdoor air quality has deteriorated in the crowded urban areas of Turkey. Elaz?? city, which is located in the east Anatolia region of Turkey, is also influenced by air pollutants. In the present study, relationship between monitored air pollutant concentrations such as SO₂ and the total suspended particles (TSP) data and meteorological factors such as wind speed, temperature, relative humidity, solar radiation and atmospheric pressure was investigated in months of October, November, December, January, February, and March during the period of 3 years (2003, 2004 and 2005) for Elaz?? city. According to the results of linear and non-linear regression analysis, it was found that there is a moderate and weak level of relation between the air pollutant concentrations and the meteorological factors in Elaz?? city. The correlation between the previous day’s SO₂, TSP concentrations and actual concentrations of these pollutants on that day was investigated and the coefficient of determination R² was found to be 0.64 and 0.54, respectively. The statistical models of SO₂ and TSP including all of meteorological parameters gave R² of 0.20 and 0.12, respectively. Further, in order to develop this model, previous day’s SO₂ and TSP concentrations were added to the equations. The new model for SO₂ and TSP was improved considerably with R²?=?0.74 and 0.61, respectively.