Down deadwood (DDW) is a carbon component important in the function and structure of forest ecosystems, but estimating DDW is problematic because these data are not widely available in forest inventory databases. However, DDW data were collected on USDA Forest Service Forest Inventory and Analysis (FIA) plots during Maine's 1995 inventory. This study examines ways to predict DDW biomass from other FIA variables so that DDW could be estimated without tedious measurement. Our results include a regression model that predicts DDW as a function of stand size class, basal area of dead and cut trees, and dummy variables for forest type and forest industry ownership. We also found DDW similar to FIA's standing-tree mortality at a statewide scale.

Particulate matter (PM) emission is one of the leading environmental pollution issues associated with the coal mining industry. Before any control techniques can be employed, however, an accurate prediction of PM concentration is desired. Towards this end, this work aimed to provide an accurate estimation of PM concentration using a hybrid machine-learning technique. The proposed predictive model was based on the hybridazation of random forest (RF) model particle swarm optimization (PSO) for estimating PM concentration. The main objective of hybridazing the PSO was to tune the hyper-parameters of the RF model. The hybrid method was applied to PM data collected from an open-cut coal mine in northern China, the Haerwusu Coal Mine. The inputs selected were wind direction, wind speed, temperature, humidity, noise level and PM concentration at 5 min before. The outputs selected were the current concentration of PM₂.₅ (particles with an aerodynamic diameter smaller than 2.5 μm), PM₁₀ (particles with an aerodynamic diameter smaller than 10 μm) and total suspended particulate (TSP). A detailed procedure for the implementation of the RF_PSO was presented and the predictive performance was analyzed. The results show that the RF_PSO could estimate PM concentration with a high degree of accuracy. The Pearson correlation coefficients among the average estimated and measured PM data were 0.91, 0.84 and 0.86 for the PM₂.₅, PM₁₀ and TSP datasets, respectively. The relative importance analysis shows that the current PM concentration was mainly influenced by PM concentration at 5 min before, followed by humidity > temperature ≈ noise level > wind speed > wind direction. This study presents an efficient and accurate way to estimate PM concentration, which is fundamental to the assessment of the atmospheric quality risks emanating from open-cut mining and the design of dust removal techniques.

Concern regarding the impacts of continued nitrogen and sulfur deposition on ecosystem health has prompted the development of critical acid load assessments for forest soils. A critical acid load is a quantitative estimate of exposure to one or more pollutants at or above which harmful acidification-related effects on sensitive elements of the environment occur. A pollutant load in excess of a critical acid load is termed exceedance. This study combined a simple mass balance equation with national-scale databases to estimate critical acid load and exceedance for forest soils at a 1-km2 spatial resolution across the conterminous US. This study estimated that about 15% of US forest soils are in exceedance of their critical acid load by more than 250 eq ha-1 yr-1, including much of New England and West Virginia. Very few areas of exceedance were predicted in the western US. This simple mass balance equation estimated that 17% of US forest soils exceed their critical acid load by more than 250 eq ha-1 yr-1, and these areas are predominantly located in the northeastern US.

Simple mass balance equations (SMBE) of critical acid loads (CAL) in forest soil were developed to assess potential risks of air pollutants to ecosystems. However, to apply SMBE reliably at large scales, SMBE must be tested for adequacy and uncertainty. Our goal was to provide a detailed analysis of uncertainty in SMBE so that sound strategies for scaling up CAL estimates to the national scale could be developed. Specifically, we wanted to quantify CAL uncertainty under natural variability in 17 model parameters, and determine their relative contributions in predicting CAL. Results indicated that uncertainty in CAL came primarily from components of base cation weathering (BCw; 49%) and acid neutralizing capacity (46%), whereas the most critical parameters were BCw base rate (62%), soil depth (20%), and soil temperature (11%). Thus, improvements in estimates of these factors are crucial to reducing uncertainty and successfully scaling up SMBE for national assessments of CAL.

The biodegradation kinetics of anionic (sodium laureth sulfate - SLES), amphoteric (disodium cocoamphodiacetate - DSCADA), and nonionic surfactants (polyalcohol ethoxylate - PAE) were assessed in this laboratory study. Similar degradation behavior was observed for all surfactants with only a fraction of the parent compound readily biodegradable. Biodegradation, as estimated by COD removal, was initially (i.e., within 24 h) rapid, however only 40-70% of the surfactant molecules were readily biodegradable. Intrinsic kinetic parameters were successfully quantified for the readily biodegradable component of the surfactant. Inhibition was not observed and microbial kinetics of SLES, DSCADA, and PAE degradation fit the Monod model well. Average μ-S curves were generated for each surfactant. Based on these results, complete degradation of the target surfactants using biological waste treatment would be limited.

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous pollutants in the urban atmosphere. In particular, atmospheric pollution has increasingly become severe in China due to its rapid urbanization and industrialization. In recent years, a few studies have presented information about POPs (such as PAHs, PCBs, OCPs) in aerosols at a molecular level in a limited number of cities such as Beijing, Qingdao and Guangzhou, as well as Hong Kong. Whereas, these cities are located in northern and southern China, respectively, where characteristics of atmospheric pollution might be different from those in the eastern cities, such as Shanghai. Atmospheric particle pollution is a persistent problem in Shanghai, a typical metropolis of China, which has several huge industrial regions. In order to gain a comprehensive understanding of the present state, properties and sources of PAHs pollution in Shanghai, PM₁₀ samples were collected at Coal-Fired Power Plant (CFP), Chlor-Alkali Chemical factory (CAC) and Coking and Chemical factory (CCF) in an industrial area, during the period, November 2004-September 2005. The concentrations of 16 PAHs were analyzed using the HPLC with UV visible detector. The results showed that the mean value of total PAHs in the industrial area was 64.85 ng m-³; 3-ring PAHs were found at low levels, while 4-, 5- and 6-ring PAHs were found at high levels. The levels of BaP were 3.07 and 7.16 ng m-³ at Chlor-Alkali Chemistry Factory and Coking and Chemistry Factory sites, respectively. PAHs levels exhibited distinct seasonal variation, with the highest level in autumn and the lowest in summer. The major source of PAHs at the industrial area was fossil fuel combustion, coal-burning, industrial furnaces including others. There was a very significant correlation of PAHs levels between CCF and CAC (R ² = 0.91). The average concentration of BaP in the industrial area during the sampling period was 5.95 ng m-³. It could be concluded the local population appears to be exposed to significantly high cancer risk (exceeding 2 ng m-³ in autumn and winter) as compared to the population of other areas.