An integrated fuzzy-stochastic modeling approach for risk assessment of groundwater contamination

An integrated fuzzy-stochastic risk assessment (IFSRA) approach was developed in this study to systematically quantify both probabilistic and fuzzy uncertainties associated with site conditions, environmental guidelines, and health impact criteria. The contaminant concentrations in groundwater predicted from a numerical model were associated with probabilistic uncertainties due to the randomness in modeling input parameters, while the consequences of contaminant concentrations violating relevant environmental quality guidelines and health evaluation criteria were linked with fuzzy uncertainties. The contaminant of interest in this study was xylene. The environmental quality guideline was divided into three different strictness categories: "loose", "medium" and "strict". The environmental-guideline-based risk (ER) and health risk (HR) due to xylene ingestion were systematically examined to obtain the general risk levels through a fuzzy rule base. The ER and HR risk levels were divided into five categories of "low", "low-to-medium", "medium", "medium-to-high" and "high", respectively. The general risk levels included six categories ranging from "low" to "very high". The fuzzy membership functions of the related fuzzy events and the fuzzy rule base were established based on a questionnaire survey. Thus the IFSRA integrated fuzzy logic, expert involvement, and stochastic simulation within a general framework. The robustness of the modeling processes was enhanced through the effective reflection of the two types of uncertainties as compared with the conventional risk assessment approaches. The developed IFSRA was applied to a petroleum-contaminated groundwater system in western Canada. Three scenarios with different environmental quality guidelines were analyzed, and reasonable results were obtained. The risk assessment approach developed in this study offers a unique tool for systematically quantifying various uncertainties in contaminated site management, and it also provides more realistic support for remediation-related decisions.     

Impacts on air pollution in urban areas

Environmental management involves controlling various forms of pollution to levels that do not pose a threat to the health of the people and the environment in general. This paper presents a framework to analyze sources of local air pollution in cities. Using an OLS model, an investigation is performed of the relationship among the concentrations of air pollutants [more precisely, concentrations of sulfur dioxide (SO₂), dust, nitric oxide (NO), nitrogen dioxide (NO₂), carbon oxide (CO) and ozone (O₃)], economic activities, and meteorology. Time series analysis leads to a model, that explains a high degree of the variance in the air pollution data. The model is applied to daily time series from three measurement stations in innsbruck, Austria. Estimation results of the model generally fit with the expected relations. Space heating influences SO₂, dust, and NO, while NO₂ levels are primarily affected by traffic. These results also indicate interdependent relations among the pollutants NO, NO₂, O₃, and CO; O₃ levels depend on temperature and sunshine.     

Identifying Riparian Buffer Effects on Stream Nitrogen in Southeastern Coastal Plain Watersheds

Within the Southeastern (SE) Coastal Plain of the U.S., numerous freshwaters and estuaries experience eutrophication with significant nutrient contributions by agricultural non-point sources (NPS). Riparian buffers are often used to reduce agricultural NPS yet the effect of buffers in the watershed is difficult to quantify. Using corrected Akaike information criterion (AIC_c) and model averaging, we compared flow-path riparian buffer models with land use/land cover (LULC) models in 24 watersheds from the SE Coastal Plain to determine the ability of riparian buffers to reduce or mitigate stream total nitrogen concentrations (TNC). Additional models considered the relative importance of headwaters and artificial agricultural drainage in the Coastal Plain. A buffer model which included cropland and non-buffered cropland best explained stream TNC (R ² = 0.75) and was five times more likely to be the correct model than the LULC model. The model average predicted that current buffers removed 52 % of nitrogen from the edge-of-field and 45 % of potential nitrogen from the average SE Coastal Plain watershed. On average, 26 % of stream nitrogen leaked through buffered cropland. Our study suggests that stream TNC could potentially be reduced by 34 % if buffers were adequately restored on all cropland. Such estimates provide realistic expectations of nitrogen removal via buffers to watershed managers as they attempt to meet water quality goals. In addition, model comparisons of AIC_c values indicated that non-headwater buffers may contribute little to stream TNC. Model comparisons also indicated that artificial drainage should be considered when accessing buffers and stream nitrogen.     

Predicting Carbon Monoxide Concentrations in the Air of Pardis City,Iran, Using an Artificial Neural Network

To date, several methods have been proposed to explain the complex process of air pollution prediction. One of these methods uses neural networks. Artificial neural networks (ANN) are a branch of artificial intelligence, and because of their nonlinear mathematical structures and ability to provide acceptable forecasts, they have gained popularity among researchers. The goal of our study as documented in this article was to compare the abilities of two different ANNs, the multilayer perceptron (MLP) and radial basis function (RBF) neural networks, to predict carbon monoxide (CO) concentrations in the air of Pardis City, Iran. For the study, we used data collected hourly on temperature, wind speed, and humidity as inputs to train the networks. The MLP neural network had two hidden layers that contained 13 neurons in the first layer and 25 neurons in the second layer and reached a mean bias error (MBE) of 0.06. The coefficient of determination (R²), index of agreement (IA), and the Nash–Scutcliffe efficiency (E) between the observed and predicted data using the MLP neural network were 0.96, 0.9057, and 0.957, respectively. The RBF neural network with a hidden layer containing 130 neurons reached an MBE of 0.04. The R², IA, and E between the observed and predicted data using the RBF neural network were 0.981, 0.954, and 0.979, respectively. The results provided by the RBF neural network had greater acceptable accuracy than was the case with the MLP neural network. Finally, the results of a sensitivity analysis using the MLP neural network indicated that temperature is the primary factor in the prediction of CO concentrations and that wind speed and humidity are factors of second and third importance when forecasting CO levels.     

Air quality impact of carbon monoxide emission from diesel engine electric power generators

An emissions inventory and the AERMOD View dispersion model were used to estimate the concentrations and the potential effects of carbon monoxide (CO) from diesel engine electric power generators operated by and providing electricity to a textile factory in Nigeria on its host air shed. The CO emissions from simultaneous operations of all of the electric power generators in the factory resulted in: 1‐hr average CO emissions of 4.2 to 54.5 micrograms per cubic meters (μg/m³) and 24‐hr average CO emissions of 0.3 to 20.9 μg/m³. The estimated 1‐hr averaging period maximum ground‐level concentrations of CO were deposited within the factory, while the 24‐hr maximum ground‐level concentrations are estimated at a distance 90 meters (m) from the factory in a southeast direction. The ground‐level concentrations of CO emanating from the textile factory are within the stipulated ambient air quality standards.     

SO2 Dispersion and Monthly Evaluation of the Industrial Source Complex Short-Term (ISCST32) Model at Mina Al-Fahal Refinery,Sultanate of Oman

Modeling of air pollutant dispersion has been undertaken for emissions of sulfur dioxide (SO₂) at the Mina Al-Fahal refinery in the Sultanate of Oman. The study was conducted during the period of November 1999 to October 2000. The Industrial Source Complex Short-Term (ISCST32) air pollution model was adopted to predict the ground level concentration of SO₂ in and around the refinery. The modeling results were validated against measured data during the study period. The comparison, based on the monthly average measurements, showed that the model underestimates the observed SO₂ concentrations. However, the predicted ground level concentrations of SO₂ during the months of September, October, November, and June were in better agreement with the observations. The predicted SO₂ values are presented in the form of concentration contours to determine the spatial distribution of SO₂ and to assess the impact on air quality over the survey area. Predicted SO₂ concentrations were found lower than the World Health Organisation (WHO) guideline value of 365 μg/m³, with the maximum ground level concentrations being found to occur relatively close to the sources of emission. Moreover, concentration contour patterns for the modeled area vary with changes in meteorological conditions. On the basis of this study, the refinery is not likely to cause any significant deterioration in air quality, and predicted concentrations of SO₂ are well below those likely to influence health.     

The connecticut indirect source review: A methodology and model for evaluating CO concentrations

The body of information presented in this paper is directed to air pollution engineers who are concerned with the effect of indirect sources on ambient concentrations of carbon monoxide (CO). Data taken under controlled conditions are used to empirically derive and calibrate a model for predicting CO concentrations in the vicinity of roadway intersections and other points of possible vehicular congestion. Since the predicted free flow CO contribution of vehicles traveling at normal road speeds is relatively low, it is concluded that idling vehicles at points of congestion are the major cause of CO violations, and that state and federal programs should place more emphasis on relieving congestion and reducing idling emission rates in new vehicles.     

LONG-TERM EQUILIBRIUM PHOSPHORUS CONCENTRATIONS IN THE EVERGLADES AS PREDICTED BY A VOLLENWEIDER-TYPE MODEL1

ABSTRACT: Anthropogenic phosphorus loading, mainly from the Everglades Agricultural Area (EAA), is believed to be the primary cause of eutrophication in the Everglades. The state of Florida has adopted a plan for addressing Everglades eutrophication problems by reducing anthropogenic phosphorus loads through the implementation of Best Management Practices (BMPs) in agricultural watersheds and the construction of stormwater treatment areas (STAs). Optimizing the effectiveness of these STAs for reducing phosphorus concentrations from agricultural runoff is a critical component of the District's comprehensive Everglades protection effort. Therefore, the objective of this study was to develop a simple tool that can be used to estimate STAs’performance and evaluate management alternatives considered in the Everglades restoration efforts. The model was tested at two south Florida wetland sites and then was used to simulate several management alternatives and predict ecosystem responses to reduced external phosphorus (P) loadings. Good agreement between model predictions at the two wetland sites and actual observations indicated that the model can be used as a management tool to predict wetlands’response to reductions in external phosphorus load and long-term P levels in aquatic ecosystems. Model results showed that lowering P content of the Everglades Protection Area (EPA) depends on reducing P loads originating from EAA discharges, not from rainfall. Assuming no action is taken (e.g., no BMPs or STAs implemented), the steady state model predicted that the average concentration within the modeled area of the marsh would reach 20 μg L^?1 within five years. With an 85 percent reduction in P loading, the steady-state model predicted that Water Conservation Area 2A (WCA-2A) P concentration will equilibrate at approximately 10 μ L^?1, while elimination of all loadings is projected to further reduce marsh P to values less than 10 μg L^?1.     

An Ecophysiological Study of Plants Growing on the Fly Ash Deposits from the “Nikola Tesla–A” Thermal Power Station in Serbia

This ecophysiological research on the ash deposits from the Nikola Tesla–A thermal power station in Serbia covered 10 plant species (Tamarix gallica, Populus alba, Spiraea van-hauttei, Ambrosia artemisifolia, Amorpha fruticosa, Eupatorium cannabinum, Crepis setosa, Epilobium collinum, Verbascum phlomoides, and Cirsium arvense). This paper presents the results of a water regime analysis, photosynthetic efficiency and trace elements (B, Cu, Mn, Zn, Pb, and Cd) content in vegetative plant parts. Water regime parameters indicate an overall stability in plant-water relations. During the period of summer drought, photosynthetic efficiency (Fv/Fm) was low, ranging from 0.429 to 0.620 for all the species that were analyzed. An analysis of the tissue trace elements content showed a lower trace metal concentration in the plants than in the ash, indicating that heavy metals undergo major concentration during the combustion process and some are not readily taken up by plants. The Zn and Pb concentrations in all of the examined species were normal whereas Cu and Mn concentrations were in the deficiency range. Boron concentrations in plant tissues were high, with some species even showing levels of more than 100 g/g (Populus sp., Ambrosia sp., Amorpha sp., and Cirsium sp.). The presence of Cd was not detected. In general, it can be concluded from the results of this research that biological recultivation should take into account the existing ecological, vegetation, and floristic potential of an immediate environment that is abundant in life forms and ecological types of plant species that can overgrow the ash deposit relatively quickly. Selected species should be adapted to toxic B concentrations with moderate demands in terms of mineral elements (Cu and Mn).     

Surface ozone and carbon monoxide levels observed at Oki, Japan: regional air pollution trends in East Asia

Simultaneous ground-based measurements of ozone and carbon monoxide were performed at Oki, Japan, from January 2001 to September 2002 in order to investigate the O(3) and CO characteristics and their distributions. The observations revealed that O(3) and CO concentrations were maximum in springtime and minimum in the summer. The monthly averaged concentrations of O(3) and CO were 60 and 234 ppb in spring and 23 and 106 ppb in summer, respectively. Based on direction, 5-day isentropic backward trajectory analysis was carried out to determine the transport path of air masses, preceding their arrival at Oki. Comparison between classified results from present work and results from the year 1994-1996 was carried out. The O(3) and CO concentration results of classified air masses in our analysis show similar concentration trends to previous findings; highest in the WNW/W, lowest in N/NE and medium levels in NW. Moreover, O(3) levels are higher and CO levels are lower in the present study in all categories.     

Radiant Domestic Combustion Stove System: Experimental and Simulated Study of Energy Use and Thermal Comfort

A steady-state space radiant heat model and a stove combustion model are developed to simulate the heat exchanges between various surfaces in the room and the stove and stack surfaces, assuming stiochiometric combustion inside the stove and the exhaust gases flow out through the stack by natural convection. The space heat model calculates the fuel consumption, the stove, stack temperatures, and the mass flow rate of exhaust gases, and provides an opportunity to study the energy efficiency of the stove, while satisfying the constraints of thermal comfort. Fanger (1982) Fanger, P. O. 1982. Thermal Comfort Analysis and Applications in Engineering, 156–198. New York: McGraw Hill.  [Google Scholar] model and a radiation exchange model between various surfaces of the space, the thermal building energy balance, and stove combustion process is applied to determine the mean radiant temperature (MRT) and the extent of thermal comfort as determined by predicted mean vote (PMV).

The overall model is validated by performing experiments in a room placed inside a controlled outdoor environment. The room is heated using a domestic stove common for rural areas of Lebanon and the MRT, the room air temperature, the walls and window temperatures are measured at two stove positions. The measured MRT, the average room temperature, and the wall surface temperatures agreed within ±7% of values predicted by the numerical model.

A parametric study is performed to optimize the stove and occupant locations in the room where adequate comfort level can be maintained at lower fuel consumption levels. The values of MRT and PMV depend strongly on the position of the radiant stove heater and stack with respect to the cold window and the occupant location. It is shown that it is possible to save up to 15% in stove fuel consumption by changing the stove position in the room with respect to the window and to the person, while maintaining the same level of comfort.     

Water quality monitoring—Some practical sampling frequency considerations

Water quality monitoring involves sampling a population, water quality, that is changing over time. Sample statistics (e.g., sample mean) computed from data collected by a monitoring network can be affected by three general factors: (1) random changes due to storms, rainfall, etc.; (2) seasonal changes in temperature, rainfall, etc.; and (3) serial correlation or duplication in information from sample to sample. (Closely spaced samples will tend to give similar information).In general, these effects have been noted, but their specific effects on water quality monitoring network design have not been well defined quantitatively. The purpose of this paper is to examine these effects with a specific data set and draw conclusions relative to sampling frequency determinations in network design.The design criterion adopted for this study of effects due to the above factors is the width of confidence intervals about annual sample geometric means of water quality variables. The data base for the study consisted of a daily record of 5 water quality variables at 9 monitoring stations in Illinois for a period of 1 year.Three general regions of frequencies were identified: (1) greater than approximately 30 samples per year where serial correlation plays a dominant role; (2) between approximately 10 and 30 samples per year where the effects of seasonal variation and serial correlation tended to cancel each other out; and (3) less than approximately 10 samples per year where seasonal variation plays a dominant role. In region 2, either seasonal variation and serial correlation should both be considered or both ignored. To consider only seasonal variation introduces more error than ignoring it. These results are network averages (over variables and stations) from one network, thus results for individual variables may deviate considerably from the average and from those for other networks.Financial support for this study was provided, in part, by the U.S. Environmental Protection Agency, grant number R805759-01-0.     

A MANGANESE OXIDATION MODEL FOR RIVERS1

ABSTRACT: The presence of manganese in natural waters (>0.05 mg/L) degrades water-supply quality. A model was devised to predict the variation of manganese concentrations in river water released from an impoundment with the distance downstream. The model is one-dimensional and was calibrated using dissolved oxygen, biochemical oxygen demand, pH, manganese, and hydraulic data collected in the Duck River, Tennessee. The results indicated that the model can predict manganese levels under various conditions. The model was then applied to the Chattahoochee River, Georgia. Discrepancies between observed and predicted may be due to inadequate pH data, precipitation of sediment particles, unsteady flow conditions in the Chattahoochee River, inaccurate rate expressions for the low pH conditions, or their combinations.     

Non-Point-Source Impacts on Stream Nutrient Concentrations Along a Forest to Urban Gradient

We conducted statistical analyses of a 10-year record of stream nutrient and sediment concentrations for 17 streams in the greater Seattle region to determine the impact of urban non-point-source pollutants on stream water quality. These catchments are dominated by either urban (22–87%) or forest (6–73%) land cover, with no major nutrient point sources. Stream water phosphorus concentrations were moderately strongly (r²=0.58) correlated with catchment land-cover type, whereas nitrogen concentrations were weakly (r²=0.19) and nonsignificantly (at < 0.05) correlated with land cover. The most urban streams had, on average, 95% higher total phosphorus (TP) and 122% higher soluble reactive phosphorus (SRP) and 71% higher turbidity than the most forested streams. Nitrate (NO₃), ammonium (NH₄), and total suspended solids (TSS) concentrations did not vary significantly with land cover. These results suggest that urbanization markedly increased stream phosphorus concentrations and modestly increased nitrogen concentrations. However, nutrient concentrations in Seattle region urban streams are significantly less than those previously reported for agricultural area streams.     

MODELING TO EVALUATE MACROPHYTE INDUCED IMPACTS TO DISSOLVED OXYGEN IN A TAILWATER RESERVOIR1

ABSTRACT: Effects of aquatic macrophytes are not considered in most standard water quality models. This study used field measurements and water quality models to help determine the effects of aquatic macrophytes on dissolved oxygen (DO) concentrations in a shallow tailwater reservoir. Installation of a hydropower plant and macrophytes (primarily Potamogeton and Chara) in a large shallow portion of the lake are possible causes of reduced DO levels in the tailwater reservoir. A water quality model (WASP5) was used to quantify the various DO sources and sinks and to evaluate the effects of the hydropower operations on DO levels in the lake. It was found that the macrophytes in Lake Ogallala had a significant effect on the DO levels in the lake. At an average macrophyte density of about 6,360 g/m² (wet weight) in 2000, the DO fluctuated daily from about 3 mg/l to about 12 mg/l. At an average macrophyte density of about 2,120 g/m² (wet weight) in 2002, the DO fluctuated from about 5 mg/l to about 9 mg/l daily. The model predicted that the DO would remain near 5 mg/l without macrophytes. The photo‐synthetic and respiration rates developed in the model (4.4 mg/g‐hr and 1.4 mg/g‐hr, respectively) agree well with literature values.     

SO2 dispersion and monthly evaluation of the industrial source complex short-term (ISCST32) model at Mina Al-Fahal refinery,Sultanate of Oman

Modeling of air pollutant dispersion has been undertaken for emissions of sulfur dioxide (SO(2)) at the Mina Al-Fahal refinery in the Sultanate of Oman. The study was conducted during the period of November 1999 to October 2000. The Industrial Source Complex Short-Term (ISCST32) air pollution model was adopted to predict the ground level concentration of SO(2) in and around the refinery. The modeling results were validated against measured data during the study period. The comparison, based on the monthly average measurements, showed that the model underestimates the observed SO(2) concentrations. However, the predicted ground level concentrations of SO(2) during the months of September, October, November, and June were in better agreement with the observations. The predicted SO(2) values are presented in the form of concentration contours to determine the spatial distribution of SO(2) and to assess the impact on air quality over the survey area. Predicted SO(2) concentrations were found lower than the World Health Organisation (WHO) guideline value of 365 microg/m(3), with the maximum ground level concentrations being found to occur relatively close to the sources of emission. Moreover, concentration contour patterns for the modeled area vary with changes in meteorological conditions. On the basis of this study, the refinery is not likely to cause any significant deterioration in air quality, and predicted concentrations of SO(2) are well below those likely to influence health.     

CLIMATE CHHANGE SENSITIVITY ASSESSMENT ON UPPER MISSISSIPPI RIVER BASIN STREAMFLOWS USING SWAT1

ABSTRACT: The Soil and Water Assessment Tool (SWAT) model was used to assess the effects of potential future climate change on the hydrology of the Upper Mississippi River Basin (UMRB). Calibration and validation of SWAT were performed using monthly stream flows for 1968–1987 and 1988–1997, respectively. The R² and Nash‐Sutcliffe simulation efficiency values computed for the monthly comparisons were 0.74 and 0.69 for the calibration period and 0.82 and 0.81 for the validation period. The effects of nine 30‐year (1968 to 1997) sensitivity runs and six climate change scenarios were then analyzed, relative to a scenario baseline. A doubling of atmospheric CO₂ to 660 ppmv (while holding other climate variables constant) resulted in a 36 percent increase in average annual streamflow while average annual flow changes of ?49, ?26, 28, and 58 percent were predicted for precipitation change scenarios of ?20, ?10, 10, and 20 percent, respectively. Mean annual streamflow changes of 51,10, 2, ?6, 38, and 27 percent were predicted by SWAT in response to climate change projections generated from the CISRO‐RegCM2, CCC, CCSR, CISRO‐Mk2, GFDL, and HadCMS general circulation model scenarios. High seasonal variability was also predicted within individual climate change scenarios and large variability was indicated between scenarios within specific months. Overall, the climate change scenarios reveal a large degree of uncertainty in current climate change forecasts for the region. The results also indicate that the simulated UMRB hydrology is very sensitive to current forecasted future climate changes.     

REGIONAL HYDROLOGIC RESPONSE OF LOBLOLLY PINE TO AIR TEMPERATURE AND PRECIPITATION CHANGES1

ABSTRACT: Large deviations in average annual air temperatures and total annual precipitation were observed across the southern United States during the last 50 years, and these fluctuations could become even larger during the next century. We used PnET-IIS, a monthly time-step forest process model that uses soil, vegetation, and climate inputs to assess the influence of changing climate on southern U.S. pine forest water use. After model predictions of historic drainage were validated, the potential influences of climate change on loblolly pine forest water use was assessed across the region using historic (1951 to 1984) monthly precipitation and air temperature which were modified by two general circulation models (GCMs). The GCMs predicted a 3.2°C to 7.2°C increase in average monthly air temperature, a -24 percent to + 31 percent change in monthly precipitation and a -1 percent to + 3 percent change in annual precipitation. As a comparison to the GCMs, a minimum climate change scenario using a constant 2°C increase in monthly air temperature and a 20 percent increase in monthly precipitation was run in conjunction with historic climate data. Predicted changes in forest water drainage were highly dependent on the GCM used. PnET-IIS predicted that along the northern range of loblolly pine, water yield would decrease with increasing leaf area, total evapotranspiration and soil water stress. However, across most of the southern U.S., PnET-IIS predicted decreased leaf area, total evapotranspiration, and soil water stress with an associated increase in water yield. Depending on the GCM and geographic location, predicted leaf area decreased to a point which would no longer sustain loblolly pine forests, and thus indicated a decrease in the southern most range of the species within the region. These results should be evaluated in relation to other changing environmental factors (i.e., CO₂ and O₃) which are not present in the current model.     

西南地区高校冬季室内二氧化碳浓度的测试研究

以西南科技大学为研究对象，用便携式红外线二氧化碳分析仪对其冬季室内CO2浓度进行测试分析，测试大学教室课前课后及寝室有人状态下CO2的浓度，包括普查检测以及连续监测。普查检测结果显示20％的样本CO2超过国家规定的上限值（0．10％），且超标的样本在检测时都处于通风状态不佳时；连续监测则反映出室内CO2的增长与室内人数、房间体积以及通风情况的关系，建议尽量减小室内人员密度，增强通风，以此来确保高校师生身体健康。     

Temporal‐spatial variations of air pollutants in Lanzhou,Gansu Province,China, during the spring–summer periods, 2014–2016

In this article, we analyzed the mass concentrations of particulate matter 2.5 micrometers (µm) or less in size (PM_2.5), particulate matter 10 µm or less in size (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), and ozone (O₃) in Lanzhou, the capital of Gansu province, China. We analyzed monitoring data collected from five air quality monitoring stations during the spring–summer period from 2014 to 2016. Our comparison of contaminant concentrations and average diurnal, daily, monthly, and annual concentrations revealed that the average concentrations of PM_2.5 and PM₁₀ amounted to 128.57 and 46.4 micrograms per cubic meters (µg/m³), respectively, exceeding the Chinese National Ambient Air Quality Standard (NAAQS). We used the Pearson correlation coefficient to establish connections between particulate matter and gaseous pollutants. The results show significant differences in the concentration levels of airborne pollutants. The Pearson correlation coefficient between PM_2.5 and PM₁₀ had the highest coefficient of r = 0.842. A correlation between the two particulate matter sizes (PM_2.5 and PM₁₀) and SO₂ was PM_2.5 and SO₂ r = 0.313; PM₁₀ and SO₂ r = 0.279; and CO and the two particulate matter sizes, PM_2.5 and CO r = 0.304; and PM₁₀ and CO r = 0.203. The average monthly ratio for the study months of PM_2.5 to PM₁₀ was 0.361. In addition, we used the hybrid single particle Lagrangian integrated trajectory model for tracking sources and pathways of the air pollutants in Lanzhou.