Parks with various types of vegetations played an important role in ameliorating air quality in urban areas. However, the attenuation effect of urban vegetation on levels of air pollution was rarely been experimentally estimated. This study, using seasonal monitoring data of total suspended particles (TSP), sulfur dioxide (SO₂) and nitrogen dioxide (NO₂) from six parks in Pudong District, Shanghai, China, demonstrated vegetations in parks can remove large amount of airborne pollutants. In addition, crown volume coverage (CVC) was introduced to characterize vegetation conditions in parks and a mixed-effects model indicated that CVC and the pollution diffusion distance were key predictors influencing pollutants removal rate. Therefore, it could be estimated by regression analysis that in summer, urban vegetations in Pudong District could contribute to 9.1% of TSP removal, 5.3% of SO₂ and 2.6% of NO₂. The results could be considered for a better park planning and improving air quality.

A comprehensive sampling campaign was carried out to study atmospheric concentration of polycyclic aromatic hydrocarbons (PAHs) in Beijing and to evaluate the effectiveness of source control strategies in reducing PAHs pollution after the 29th Olympic Games. The sub-cooled liquid vapor pressure (logPL ᵒ)-based model and octanol-air partition coefficient (Kₒₐ)-based model were applied based on each seasonal dateset. Regression analysis among log KP, logPL ᵒ and log Kₒₐ exhibited high significant correlations for four seasons. Source factors were identified by principle component analysis and contributions were further estimated by multiple linear regression. Pyrogenic sources and coke oven emission were identified as major sources for both the non-heating and heating seasons. As compared with literatures, the mean PAH concentrations before and after the 29th Olympic Games were reduced by more than 60%, indicating that the source control measures were effective for reducing PAHs pollution in Beijing.

In the present study, five soil samples with organic carbon contents ranging from 0.23% to 7.1% and aged with technical dichlorodiphenyltrichloroethane (DDT) and hexachlorocyclohexane (HCH) for 15 months were incubated in a sealed chamber to investigate the dynamic changes of the OCP residues. The residues in the soils decreased over the incubation period and finally reached a plateau. Regression analysis showed that degradable fractions of OCPs were negatively correlated with soil organic carbon (SOC) except for α-HCH, while no correlation was found between degradation rate and SOC, which demonstrated that SOC content determines the OCP sequestration fraction in soil. Analysis of the ratio of DDT and its primary metabolites showed that, since it depends on differential sequestration among them, magnitude of (p,p′-DDE + p,p′-DDD)/p,p′-DDT is not a reliable criterion for the identification of new DDT sources.

One monitoring station is insufficient to characterize the high spatial variation of traffic-related heavy metals within cities. We tested moss bags (Hylocomium splendens), deployed in a dense network, for the monitoring of metals in outdoor air and characterized metals’ long-term spatial distribution and its determinants in Girona, Spain. Mosses were exposed outside 23 homes for two months; NO₂ was monitored for comparison. Metals were not highly correlated with NO₂ and showed higher spatial variation than NO₂. Regression models explained 61–85% of Cu, Cr, Mo, Pb, Sb, Sn, and Zn and 72% of NO₂ variability. Metals were strongly associated with the number of bus lines in the nearest street. Heavy metals are an alternative traffic-marker to NO₂ given their toxicological relevance, stronger association with local traffic and higher spatial variability. Monitoring heavy metals with mosses is appealing, particularly for long-term exposure assessment, as mosses can remain on site many months without maintenance.

The experiments were done to investigate the effect of soil pH and organic matter content on EDTA-extractable heavy metal contents in soils and heavy metal concentrations in rice straw and grains. EDTA-extractable Cr contents in soils and concentrations in rice tissues were negatively correlated with soil pH, but positively correlated with organic matter content. The combination of soil pH and organic matter content would produce the more precise regression models for estimation of EDTA-Cu, Pb and Zn contents in soils, demonstrating the distinct effect of the two factors on the availability of these heavy metals in soils. Soil pH greatly affected heavy metal concentrations in rice plants. Furthermore, inclusion of other soil properties in the stepwise regression analysis improved the regression models for predicting straw Fe and grain Zn concentrations, indicating that other soil properties should be taken into consideration for precise predicting of heavy metal concentrations in rice plants.

Our aim was to examine the health effects on bathers from exposure to recreational seawater on three beaches with different microbiology quality and bather density through a cohort study. An initial questionnaire and a 10day follow up were used. Univariate and backward logistic regression analyses were used to asses the risk of developing symptoms or diseases among the bathers. In total, 3805 bathers were included in the study and 149 samples were collected from the three beaches. Despite all the beaches being characterized of high quality the levels of bacterial indicators differed among them. Health effects among bathers were not associated with bacterial indicators. A statistically significant increased risk for symptoms related to respiratory illness, gastroenteritis, medical consultation and use of medication was observed among bathers at the higher bather density beaches. Beach bather numbers should always be taken into account when considering a risk assessment approach.

The occurrences of increased atmospheric nitrogen deposition (ADN) in Southeast Asia during smoke haze episodes have undesired consequences on receiving aquatic ecosystems. A successful prediction of episodic ADN will allow a quantitative understanding of its possible impacts. In this study, an artificial neural network (ANN) model is used to estimate atmospheric deposition of total nitrogen (TN) and organic nitrogen (ON) concentrations to coastal aquatic ecosystems. The selected model input variables were nitrogen species from atmospheric deposition, Total Suspended Particulates, Pollutant Standards Index and meteorological parameters. ANN models predictions were also compared with multiple linear regression model having the same inputs and output. ANN model performance was found relatively more accurate in its predictions and adequate even for high-concentration events with acceptable minimum error. The developed ANN model can be used as a forecasting tool to complement the current TN and ON analysis within the atmospheric deposition-monitoring program in the region.

In situ droplet-size distributions were measured using a laser in situ scattering and transmissiometry (LISST-100X) particle size analyzer during the evaluation of natural and chemical dispersion efficiency of crude oils under different wave and current conditions. An entropy grouping of the in situ dispersed oil droplet-size spectra has classified the multi-modal droplet-size distributions into different groups based on similar droplet-size spectra characteristics within groups and distinction between groups. A generalized linear logistic regression model was fitted to analyze the effects of a number of factors and their interactions on the grouping of oil droplet-size spectra. The grouped results corresponded to the oil dispersion efficiency at different levels. This new method for droplet-size distribution data analysis can have significant implication in field evaluation of natural and chemical dispersion efficiency of oil.

The natural process of eutrophication is accelerated by human activities worldwide that interrupt nutrient biogeochemical cycles. Three algal bloom events have been monitored in the northern tributary of the Jiulong River since 2009. The inflection points in a robust locally-weighted regression analysis (LOESS) of the relationship between TN and TP concentrations in the river water, and a TN:TP comparison with nutrient source loadings, suggested that both external loading and internal nutrient cycling contributed to these algal blooms. Nutrient release from the sediments may have played an important role in regulating the nutrients in the overlying water column. In particular, excessive nutrient inputs from various sources and ubiquitous river damming caused further accumulation of the nutrient loading. In-situ autochthonous primary production was enhanced in this relatively stable “river” to “lake” water body. Thus, attention must be paid to the effects of river damming and the consequent internal nutrient release.

Currently, densities of Enterococcus in marine bathing beach samples are performed using conventional methods which require 24h to obtain results. Real-time PCR methods are available which can measure results in as little as 3h. The purpose of this study was to evaluate a more rapid test method for the determination of bacterial contamination in marine bathing beaches to better protect human health. The geometric mean of Enterococcus densities using Enterolert® defined substrate testing and membrane filtration ranged from 5.2 to 150MPN or CFU/100mL and corresponding qPCR results ranged from 6.6 to 1785CCE/100mL. The regression analysis of these results showed a positive correlation between qPCR and conventional tests with an overall correlation (r) of 0.71. qPCR was found to provide accurate and sensitive estimate of Enterococcus densities and has the potential to be used as a rapid test method for the quantification of Enterococcus in marine waters.