Comparison of land-use regression models between Great Britain and the Netherlands

Land-use regression models have increasingly been applied for air pollution mapping at typically the city level. Though models generally predict spatial variability well, the structure of models differs widely between studies. The observed differences in the models may be due to artefacts of data and methodology or underlying differences in source or dispersion characteristics. If the former, more standardised methods using common data sets could be beneficial. We compared land-use regression models for NO₂ and PM_10, developed with a consistent protocol in Great Britain (GB) and the Netherlands (NL).Models were constructed on the basis of 2001 annual mean concentrations from the national air quality networks. Predictor variables used for modelling related to traffic, population, land use and topography. Four sets of models were developed for each country. First, predictor variables derived from data sets common to both countries were used in a pooled analysis, including an indicator for country and interaction terms between country and the identified predictor variables. Second, the common data sets were used to develop individual baseline models for each country. Third, the country-specific baseline models were applied after calibration in the other country to explore transferability. The fourth model was developed using the best possible predictor variables for each country.A common model for GB and NL explained NO₂ concentrations well (adjusted R² 0.64), with no significant differences in intercept and slopes between the two countries. The country-specific model developed on common variables for NL but not GB improved the prediction.The performance of models based upon common data was only slightly worse than models optimised with local data. Models transferred to the other country performed substantially worse than the country-specific models. In conclusion, care is needed both in transferring models across different study areas, and in developing large inter-regional LUR models.     

Predicting logarithmic values of the subcooled liquid vapor pressure of halogenated persistent organic pollutants with QSPR: How different are chlorinated and brominated congeners?

Logarithmic values of the subcooled liquid vapor pressure (log P_L) were estimated for 1436 polychlorinated and polybrominated congeners of benzenes, biphenyls, dibenzo-p-dioxins, dibenzofurans, diphenyl ethers and naphthalenes by employing the Quantitative Structure–Property Relationships (QSPR) approach. The QSPR model developed with GA–PLS technique was characterized by satisfactory goodness-of-fit, robustness and the external predictive performance (R²Y = 0.970, Q_CV² = 0.970, Q_Ext² = 0.966, RMSE_C = 0.21, RMSE_CV = 0.22 and RMSE_P = 0.22). The externally validated model has been applied to predict subcooled liquid vapor pressure of uninvestigated halogenated persistent organic pollutants. Moreover, a simple arithmetic relationship between logarithmic values of subcooled liquid vapor pressures in pairs of chloro- and bromo-analogues has been found. This relationship can be used for estimating log P_L of a brominated compound, whenever log P_L of its chlorinated counterpart is known, without necessity of performing any time-consuming computations.     

A study of gas/particle partitioning of SVOCs in the tropical atmosphere of Southeast Asia

Gas- and particle-phase polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs) were collected at a tropical site in Southeast Asia over 12-h periods during November and December 2006 to determine their gas/particle distributions by analyzing integrated quartz filter and polyurethane foam samples. Gas/particle partitioning coefficients, K_p, were calculated, and their relationship with the subcooled liquid vapor pressure p_L^o for both PAHs and PCBs was investigated. The regressions of log K_p vs. log p_L^o for most of samples gave high correlations for both PAHs and PCBs and the slopes were statistically shallower than ?1, but they were relatively steeper than those obtained in temperate zones of the Northern Hemisphere. By comparison, the particle-bound fraction of low molecular weight (LMW) PAHs was underestimated by both Junge-Pankow adsorption and K_OA (octanol–air partition coefficient) absorption models, while the predicted values agree relatively better with those observed ones for high molecular weight (HMW) PAHs. In addition, the adsorption onto the soot phase (elemental carbon) predicted accurately the gas/particle partitioning of PAHs, especially for LMW compounds. On the other hand, the K_OA absorption model using the measured organic matter fraction (f_OM) value fitted the PCB data much better than the adsorption model did, indicating the sorption of nonpolar compounds to aerosols might be dominated by absorption into organic matters in this area.     

Linear and non-linear relationships between soil sorption and hydrophobicity: model, validation and influencing factors

The hydrophobic parameter represented by the octanol/water partition coefficient (log P) is commonly used to predict the soil sorption coefficient (K_oc). However, a simple non-linear relationship between log K_oc and log P has not been reported in the literature. In the present paper, soil sorption data for 701 compounds was investigated. The results show that log K_oc is linearly related to log P for compounds with log P in the range of 0.5-7.5 and non-linearly related to log P for the compounds in a wide range of log P. A non-linear model has been developed between log K_oc and log P for a wide range of compounds in the training set. This model was validated in terms of average error (AE), average absolute error (AAE) and root-mean squared error (RMSE) by using an external test set with 107 compounds. Nearly the same predictive capacity was observed in comparison with existing models. However, this non-linear model is simple, and uses only one parameter. The best model developed in this paper is a non-linear model with six correction factors for six specific classes of compounds. This model can well predict log K_oc for 701 diverse compounds with AAE = 0.37. The reasons for systemic deviations in these groups may be attributed to the difference of sorption mechanism for hydrophilic/polar compounds, low solubility for highly hydrophobic compounds, hydrolysis of esters in solution, volatilization for volatile compounds and highly experimental errors for compounds with extremely high or low sorption coefficients.     

Impacts of Meteorological Factors on PM10: Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) Approaches

In this study, prediction capacities of multi-linear regression (MLR) and artificial neural networks (ANN) onto coarse particulate matter (PM₁₀) concentrations were investigated. Different meteorological factors on particulate pollution were chosen for operating variables in the model analyses. Two different regions (urban and industrial) were identified in the region of Kocaeli, Turkey. All data sets were obtained from air quality monitoring network of the Ministry of Environment and Urban Planning, and 120 data sets were used in the MLR and ANN models. Regression equations explained the effects of the meteorological factors in MLR analyses. In the ANN model, backpropagation network with two hidden layers has achieved the best prediction efficiency. Determination coefficients and error values were examined for each model. ANN models displayed more accurate results compared to MLR.     

Agricultural solid waste for sorption of metal ions: part I—characterization and use of lettuce roots and sugarcane bagasse for Cu(II), Fe(II), Zn(II), and Mn(II) sorption from aqueous medium

Sugarcane bagasse and hydroponic lettuce roots were used as biosorbents for Cu(II), Fe(II), Zn(II), and Mn(II) removal from monoelemental solutions in aqueous medium, at pH 5.5, using batch procedures. These biomasses were studied in natura (lettuce roots, NLR, and sugarcane bagasse, NSB) and modified with HNO₃ (lettuce roots, MLR, and sugarcane bagasse, MSB). Langmuir, Freundlich, and Dubinin-Radushkevich non-linear isotherm models were used to evaluate the data from the metal ion adsorption assessment. The maximum adsorption capacities (q_max) in monoelemental solution, calculated using the Langmuir isothermal model for Cu(II), Fe(II), Zn(II), and Mn(II), were respectively 24.61, 2.64, 23.04, and 5.92 mg/g for NLR; 2.29, 16.89, 1.97, and 2.88 mg/g for MLR; 0.81, 0.06, 0.83, and 0.46 mg/g for NSB; and 1.35, 2.89, 20.76, and 1.56 mg/g for MSB. The Freundlich n parameter indicated that the adsorption process was favorable for Cu(II) uptake by NLR; Fe(II) retention by MLR and MSB; and Zn(II) sorption by NSB, MLR, and NSB and favorable for all biomasses in the accumulation of Mn(II). The Dubinin-Radushkevich isotherm was applied to estimate the energy (E) and type of adsorption process involved, which was found to be a physical one between analytes and adsorbents. Organic groups such as O–H, C–O–C, CH, and C=O were found in the characterization of the biomass by FTIR. In the determination of the biomass surface charges by using blue methylene and red amaranth dyes, there was a predominance of negative charges.     

Kinetics and isotherm analysis of Tropaeoline 000 adsorption onto unsaturated polyester resin (UPR): a non-carbon adsorbent

The presence of dyes in water is undesirable due to the toxicological impact of their entrance into the food chain. Owing to the recalcitrant nature of dyes to biological oxidation, a tertiary treatment like adsorption is required. In the present study, unsaturated polyester resin (UPR) has been used as a sorbent in the treatment of dye-contaminated water. Different concentrations of Tropaeoline 000 containing water were treated with UPR. The preliminary investigations were carried out by batch adsorption to examine the effects of pH, adsorbate concentration, adsorbent dosage, contact time, and temperature. A plausible mechanism for the ongoing adsorption process and thermodynamic parameters have also been obtained from Langmuir and Freundlich adsorption isotherm models. Thermodynamic parameter showed that the sorption process of Tropaeoline 000 onto activated carbon (AC) and UPR were feasible, spontaneous, and endothermic under studied conditions. The estimated values for (ΔG) are ?10.48?×?10³ and ?6.098?×?10³ kJ mol^?1 over AC and UPR at 303 K (30 °C), indicating towards a spontaneous process. The adsorption process followed pseudo-first-order model. The mass transfer property of the sorption process was studied using Lagergren pseudo-first-order kinetic models. The values of % removal and k _ad for dye systems were calculated at different temperatures (303–323 K). The mechanism of the adsorption process was determined from the intraparticle diffusion model.     

Estimating the vapor pressures of multi-functional oxygen-containing organic compounds using group contribution methods

A UNIFAC-based method for estimating the vapor pressure (p_L^o) values of oxygen-containing compounds of intermediate-to-low volatility has been developed as an aid in modeling the formation and behavior of organic aerosols. This UNIFAC-p_L^o method was constructed using a set of 76 compounds with experimentally determined p_L^o values. The compounds chosen are of intermediate-to-low volatility and contain multiple oxygen-containing functionalities. For test and development purposes, the 76 compounds were divided into a basis set of 43 compounds used to generate the coefficients required in the UNIFAC-p_L^o method and a second set of 33 compounds that was used to test the coefficients generated using the basis set. Both the basis and test sets contained compounds that possessed similar structures and functionalities. For the 33 compounds in the test set, on average UNIFAC-p_L^o predicted the p_L^o values to within a factor of 2 over the temperature range 290–320 K. Furthermore, the UNIFAC-p_L^o method did not show any correlation in prediction error with p_L^o so that it was equally likely to underpredict as overpredict p_L^o regardless of volatility. For comparison, three other vapor pressure estimation methods were applied to the test set of compounds. On average, these other methods all predicted the test set p_L^o values to within a factor of 3 over the temperature range 290–320 K. In contrast to the UNIFAC-p_L^o method, the prediction errors from the methods were found to be correlated with p_L^o so that the other methods overpredicted p_L^o as volatility decreased.     

Exploring relationships between outdoor air particulate-associated polycyclic aromatic hydrocarbon and PM2.5: A case study of benzo(a)pyrene in California metropolitan regions

Polycyclic aromatic hydrocarbons (PAHs) and particulate matter (PM) are co-pollutants emitted as by-products of combustion processes. Convincing evidence exists for PAHs as a primary toxic component of fine PM (PM_2.5). Because PM_2.5 is listed by the US EPA as a “Criteria Pollutant”, it is monitored regularly at sites nationwide. In contrast, very limited data is available on measured ambient air concentrations of PAHs. However, between 1999 and 2001, ambient air concentrations of PM_2.5 and benzo(a)pyrene (BaP) are available for California locations. We use multivariate linear regression models (MLRMs) to predict ambient air levels of BaP in four air basins based on reported PM_2.5 concentrations and spatial, temporal and meteorological variables as variates. We obtain an R² ranging from 0.57 to 0.72 among these basins. Significant variables (p<0.05) include the average daily PM_2.5 concentration, wind speed, temperature and relative humidity, and the coastal distance as well as season, and holiday or weekend. Combining the data from all sites and using only these variables to estimate ambient BaP levels, we obtain an R² of 0.55. These R²-values, combined with analysis of the residual error and cross validation using the PRESS-statistic, demonstrate the potential of our method to estimate reported outdoor air PAH exposure levels in metropolitan regions. These MLRMs provide a first step towards relating outdoor ambient PM_2.5 and PAH concentrations for epidemiological studies when PAH measurements are unavailable, or limited in spatial coverage, based on publicly available meteorological and PM_2.5 data.     

Correlations between fine particulate matter (PM2.5) and meteorological variables in the United States: Implications for the sensitivity of PM2.5 to climate change

We applied a multiple linear regression (MLR) model to study the correlations of total PM_2.5 and its components with meteorological variables using an 11-year (1998–2008) observational record over the contiguous US. The data were deseasonalized and detrended to focus on synoptic-scale correlations. We find that daily variation in meteorology as described by the MLR can explain up to 50% of PM_2.5 variability with temperature, relative humidity (RH), precipitation, and circulation all being important predictors. Temperature is positively correlated with sulfate, organic carbon (OC) and elemental carbon (EC) almost everywhere. The correlation of nitrate with temperature is negative in the Southeast but positive in California and the Great Plains. RH is positively correlated with sulfate and nitrate, but negatively with OC and EC. Precipitation is strongly negatively correlated with all PM_2.5 components. We find that PM_2.5 concentrations are on average 2.6 μg m^?3 higher on stagnant vs. non-stagnant days. Our observed correlations provide a test for chemical transport models used to simulate the sensitivity of PM_2.5 to climate change. They point to the importance of adequately representing the temperature dependence of agricultural, biogenic and wildfire emissions in these models.