We studied magnetic and chemical parameters of sediments from sediments of a water reservoir at Linfen (China) in order to quantitatively reconstruct the atmospheric pollution history in this region. The results show that the main magnetic phases are magnetite and maghemite originating from the surrounding catchment and from anthropogenic activities, and there is a significant positive relationship between magnetic concentration parameters and heavy metals concentrations, indicating that magnetic proxies can be used to monitor the anthropogenic pollution. In order to uncover the atmospheric pollution history, we combined the known events of environmental improvement with variations of magnetic susceptibility (χ) and heavy metals along the cores to obtain a detailed chronological framework. In addition, air comprehensive pollution index (ACPI) was reconstructed from regression equation among magnetic and chemical parameters as well as atmospheric monitoring data. Based on these results, the atmospheric pollution history was successfully reconstructed.

The removal efficiency of sediment phosphorus (P) in all fractions with a combined technology of porous ceramic filter media (PCFM) and submerged macrophytes was studied in Donghu Lake, Wuhan, China. The adsorption kinetic models of the sediment P in all fractions on PCFM could be described well by a power function equations (Qt = k · ta, 0 < a < 1). The P removal capacity of the combination of PCFM and Potamogeton crispus, a submerged macrophyte, was higher for all P forms than that of the combination of PCFM and another macrophyte, Vallisneria spiralis. This study suggested that the combination of PCFM and macrophytes could achieve a synergetic sediment P removal because the removal rates of the combinations were higher than the sum of that of PCFM and macrophytes used separately. The combined technology could be further applied to treat internal P loading in eutrophic waters.

A new approach is proposed to quantify the evaporative vapor generation based on real parking activity data. As compared to the existing methods, two improvements are applied in this new approach to reduce the uncertainties: First, evaporative vapor generation from diurnal parking events is usually calculated based on estimated average parking duration for the whole fleet, while in this study, vapor generation rate is calculated based on parking activities distribution. Second, rather than using the daily temperature gradient, this study uses hourly temperature observations to derive the hourly incremental vapor generation rates. The parking distribution and hourly incremental vapor generation rates are then adopted with Wade–Reddy's equation to estimate the weighted average evaporative generation. We find that hourly incremental rates can better describe the temporal variations of vapor generation, and the weighted vapor generation rate is 5–8% less than calculation without considering parking activity.

Measurements of isoprene emissions, solar spectral radiation, temperature and relative humidity were carried out at a grassland site in the Inner Mongolia, China during the growing seasons in 2002 and 2003. Isoprene emissions are dependent on PAR (Photosynthetically Active Radiation) and temperature nonlinearly. PAR controls the main processes related to isoprene emission, thus, PAR energy balance is used to establish quantitative relationship between isoprene emission and its affecting factors. An empirical Equation of isoprene emission was built on the basis of PAR energy balance. The calculated values were in good agreement with those measured for 2002 and 2003 summer seasons, the relative biases of 70% estimated emissions were within 50% compared to measured fluxes. The chamber changes the inside environment and emission fluxes, the emission differences were estimated by using the empirical Equation. The results show that isoprene emission flux around the noon decreases by 37% when the chamber is used, i.e., the biggest effect was resulted from PAR difference caused by the chamber. Isoprene emission measured by chamber should be corrected. The empirical model of isoprene emission showed that isoprene emission fluxes were close to zero, when PAR was low in early morning and in late evening. Total isoprene emissions emitted from the grassland in the Inner Mongolia were 1.10 and 1.00gC m−2 during the growing seasons of 2002 and 2003, respectively, which contributed to about 3.1–4.3% and 2.8−3.9% to grass respiration. The averaged isoprene emission normalized to a standard light (1 500μmolm−2 s−1) and temperature (30 °C) condition was 482.8μg m−2 h−1.

This study assessed the relationship between the satellite Aerosol Optical Depths (AODs) and the ground monitored concentrations of particulate matter (PM) mass and its major constituents (black carbon–BC, organic carbon–OC, sulfates and nitrates), respectively. Both component AOD and total AOD products of Multi–angel Imaging Spectro Radiometer (MISR) were used for comparison along with the AOD product of the Moderate Resolution Imaging Spectroradiometer (MODIS). The ground PM data available during the period from 2004 to 2010 at the Asian Institute of Technology (AIT), a suburb site of the Bangkok Metropolitan Region, was used. MODIS and MISR AODs were validated against Sun photometer AOD, monitored at the Pimai AERONET station which showed strong linear regression with high R2 values of 0.87 and 0.92, respectively. The correlation coefficients between MODIS and MISR AODs and PM mass concentrations, respectively, were improved after exclusion of observations with cloud cover above 3/10. The R values (square root of determination coefficient R2) for linear relationships between PM10 and MODIS AOD were accordingly increased from 0.33 to 0.58 for MODIS AOD and from 0.25 to 0.54 for MISR AOD, while those for PM2.5 were improved from 0.30 to 0.55 for MODIS AOD and from 0.31 to 0.43 for MISR AOD. The stepwise regression was conducted to analyze the relationship between MISR component AODs and the mass concentration of PM10 and PM2.5, respectively, as well as their constituents. Higher R values were obtained for all regression equations using MISR component AODs as compared to those using total AOD. MISR component AODs showed higher capacity for monitoring daily BC (R=0.74–0.75) and sulfates (R=0.72), as compared to nitrates (R=0.52–0.54) and hourly OC (R=0.47). The potential of MISR component AODs for ambient PM monitoring should be explored and applied in other regions.

Oil spills are one of the major sources of marine pollution; it is important to conduct comprehensive assessment of losses that occur as a result of these events. Traditional methods are required to assess the three parts of losses including cleanup, socioeconomic losses, and environmental costs. It is relatively slow because assessment is complex and time consuming. A relatively quick method was developed to improve the efficiency of assessment, and then applied to the Penglai 19-3 accident. This paper uses an SAR image to calculate the oil spill area through Neural Network Classification, and uses historical oil-spill data to build the relationship between loss and other factors including sea-surface wind speed, and distance to the coast. A multiple regression equation was used to assess oil spill damage as a function of the independent variables. Results of this study can be used for regulating and quickly dealing with oil spill assessment.

This paper represents the first study to compare seven types of first–order and one–variable grey differential equation model [abbreviated as GM (1, 1)] and back-propagation artificial neural network (BPNN) for predicting hourly particulate matter (PM) including PMio and PM2.5 concentrations in Dali area of Taichung City, Taiwan. Their prediction performance was also compared. The results indicated that the minimum mean absolute percentage error (MAPE), mean squared error (MSE), and root mean squared error (RMSE) was 16.76%, 132.95, and 11.53, respectively for PM10 prediction. For PM2.5 prediction, the minimum MAPE, MSE, and RMSE value of 21.64%, 40.41, and 6.36, respectively could be achieved. All statistical values revealed that the predicting performance of GM (1, 1, x(0)), GM (1, 1, a), and GM (1, 1, b) outperformed other GM (1, 1) models. According to the results, it revealed that GM (1, 1) could predict the hourly PM variation precisely even comparing with BPNN.

Large Eddy Simulation (LES) spectral data and Taylor statistical diffusion theory are used to obtain Eddy diffusivities in a convective boundary layer. The derivation employs a fitting expression obtained from LES data for the vertical peak frequency. The vertical Eddy diffusivities are well behaved and show similar patterns and magnitudes as those derived from experimental spectral peak frequency data. In addition, this new vertical Eddy diffusivity was introduced into an advection diffusion equation which was solved by Generalized Integral Laplace Transform Technique (GILLT) method and validated with observed contaminant concentration data of the Copenhagen experiment. The results of this new approach are shown to agree with the measurements of Copenhagen.

This work investigates the dependence of the sampling rate (Rs) of semi-permeable membrane devices (SPMDs) on flow rate and temperature. The in situ Rs values were obtained using performance reference compounds (PRCs) with weighted polynomial regression and used to estimate the bioavailable polycyclic aromatic hydrocarbon (PAH) concentrations in seawater. The in situ Rs values did not vary with flow rate and temperature. The empirical equation of the Rs value from the SPMDs was established. This infers that PRCs could be avoided by using an established empirical equation under similar field conditions. The sum of the bioavailable PAHs ranged from 0.281 to 0.611ngL−1 on the eastern side of the Taiwan Strait and from 0.438 to 1.10ngL−1 on the western side. Distinct sources and toxicity of these bioavailable PAHs were observed and mainly resulted from different types of energy consumption.

This paper analyzes the mathematical modeling procedure to describe the decomposition of adsorbed phenanthrene in prototypical and real soil samples (sand and agricultural soil, respectively) by ozone. The modeling scheme considered a set of ordinary differential equations with time varying coefficients. This model used the adsorbed ozone in the soil, the ozone reacting with the contaminant and the phenanthrene concentration in the soil sample. The main parameters involved in the mathematical model included a time varying ozone saturation function (k ₛₐₜ (t)) and reaction constants (k ᵣ). These parameters were calculated using the ozone concentration variation at the reactor output, named as ozonogram, and the measurements of phenanthrene decomposition through ozonation. The model was validated using two series of experiments: (1) soil saturated with ozone in the absence of the contaminant and (2) soil artificially contaminated with phenanthrene. In both cases, the proposed parametric identification method yields to validate the mathematical model. This fact was confirmed by the correspondence between numerical simulations and experimental data. In particular, total decomposition of phenanthrene adsorbed in two different systems (ozone-sand and ozone-agricultural soil) was obtained after 15 and 30 min of reaction, respectively. This difference was obtained as a consequence of soil physicochemical characteristics: specific surface area and pore volume. The ozonation reaction rate constants of phenanthrene in the sand and agricultural soil were calculated using the same parameter identification scheme.