Aerial Bay is one of the harbor towns of Andaman and Nicobar Islands, the union territory of India. Nevertheless, it is least studied marine environment, particularly for physico-chemical assessment. Therefore, to evaluate the annual spatiotemporal variations of physico-chemical parameters, seawater samples collected from 20 sampling stations covering three seasons were analyzed. Multivariate statistics is applied to the investigated data in an attempt to understand the causes of variation in physico-chemical parameters. Cluster analysis distinguished mangrove and open sea stations from other areas by considering distinctive physico-chemical characteristics. Factor analysis revealed 79.5% of total variance in physico-chemical parameters. Strong loading included transparency, TSS, DO, BOD, salinity, nitrate, nitrite, inorganic phosphate, total phosphorus and silicate. In addition, box-whisker plots and Geographical Information System based land use data further facilitated and supported multivariate results.

Due to lack of observational studies on greenhouse gases in Malaysia, most studies in this field were carried out based on ground station data. These studies did not utilize satellite data from the equatorial area. Satellite remote sensing is one of the most effective approaches for greenhouse gas distribution monitoring on a global scale. As such, satellite remote sensing exhibits a very high spatial and temporal resolution. Variations of ozone concentrations in Peninsular Malaysia were observed and investigated by means of data retrieved from the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY). The aim of this study was to determine the monthly and seasonal ozone concentrations in Peninsular Malaysia from January 2003 to December 2009. We utilized total column ozone at level 2 of the WFMD version 1.0. A spatial resolution value of 1°×1.25° was used. Analysis indicated that the five selected sites exhibited strong seasonal atmospheric ozone concentration variations. These variations resulted from the significant differences between the northeast monsoon (NEM) and the southwest monsoon (SWM). As the NEM prevails, cold air breaks out from Siberia and spreads to the equatorial region in the form of northeasterly cold surge winds. These winds manifest in low–level anticyclones over Southeast Asia. Inversely, the air masses from the southwest contribute to long–range air pollution. During SWM, the transport of atmospheric ozone by wind is associated with biomass burning in Sumatra, Indonesia. HYSPLIT was also utilized to identify the air pollutant transport between NEM and SWM toward Peninsular Malaysia. Comparisons were made between the ozone data from five sites in 2009. Data were retrieved from SCIAMACHY and the Atmospheric Infrared Sounder (AIRS). The relative difference average of the ozone data measured by SCIAMACHY and AIRS was approximately 6%.

Based on observations at Heshan, a boundary area in the city agglomeration of the Pearl River Delta region in China, atmospheric pollutants such as PM2.5, O3, CO, SO2, NOZ, NO2 and NO were monitored between the 12th and 29th November, 2010. Meteorological parameters, including temperature, humidity, dew point, air pressure, ultraviolet light, wind direction, and wind speed were also measured. By combining the meteorological parameters with the atmospheric pollutant data, we performed Positive Matrix Factorization (PMF) and ozone production efficiency (OPE) analysis to objectively understand the interrelations among the pollutants, as well as between the pollutants and the meteorological factors. During the observation period, there were various meteorological changes such as rainfall, cold air transit, and sunshine that created conditions for the formation or dispersal of pollutants. The study period coincided with the 16th Asian Games, during which time the government adopted strict measures to reduce the discharge of pollutants around the Pearl River Delta area. However, we still observed serious pollution of PM2.5 and O3, of which the highest value of PM2.5 was 210 μg m−3 and the highest value of O3 reached 117 ppb. At the same time, the high concentrations of CO, NO, NO2, NOZ, and SO2 could not be cleared away with rainfall in such a short period of time. On the basis of PMF analysis, we found that three factors influence the air quality of this region: local biomass burning, secondary pollutants of regional transport, and high industrial pollutant emissions. According to OPE analysis, the O3 pollution was mostly found to be VOC–sensitive but occasionally NOX–sensitive for OPE values greater than 10.

This study assessed the level of contamination of the topsoil by Pb, Cu, Cr, Cd and Zn and the spatial distribution of these heavy metals around a cement factory. Thirty–eight composite soil samples were collected around the cement factory and subjected to nitric–perchloric acid digestion. Total metal contents of the soil were determined by flame atomic absorption spectrophotometry (FAAS) and the data generated were analyzed statistically. Spatial mapping of the distribution of heavy metals was done through the use of Inverse Distance Weighted technique (IDW) of ArcGIS 10. The results showed that the contamination domain of Cd was in the extreme domain while Pb and Cu levels in the soil were in the severe and moderate contamination domains. Zn and Cr posed no potential environmental hazard because of their low level in the soil. The spatial mapping of the heavy metals indicated Pb and Cu enrichment of the soil not only to come from cement production activities but also from vehicular activities while Cd enrichment of the soil was mainly from the cement production. Mapping of Zn and Cr distribution showed that their enrichments in the soil were from cement production activities. From these findings, it is highly recommended that environmental auditing of the cement production line be carried out to reduce the release of pollutants. It is also important that remediation activities be carried out on the soil to reduce the levels of Cd, Pb and Cu to avert potential ecological disasters.

In November 2012, the Australian Pesticide and Veterinary Medicines Authority (APVMA) concluded a 12year review of the PSII herbicide diuron. One of the primary concerns raised during the review was the potential impact on aquatic ecosystems, particularly in the catchments draining to the Great Barrier Reef. The environmental risk assessment process used by the APVMA utilised a runoff risk model developed and validated under European farming conditions. However, the farming conditions in the sugarcane regions of the Great Barrier Reef catchments have environmental parameters beyond the currently validated bounds of the model. The use of the model to assess environmental risk in these regions is therefore highly inappropriate, demonstrating the pitfalls of a one size fits all approach.

Ocean acidification will have many negative consequences for marine organisms and ecosystems, leading to a decline in many ecosystem services provided by the marine environment. This study reviews the effect of ocean acidification (OA) on seagrasses, assessing how this may affect their capacity to sequester carbon in the future and providing an economic valuation of these changes. If ocean acidification leads to a significant increase in above- and below-ground biomass, the capacity of seagrass to sequester carbon will be significantly increased. The associated value of this increase in sequestration capacity is approximately £500 and 600 billion globally between 2010 and 2100. A proportionally similar increase in carbon sequestration value was found for the UK. This study highlights one of the few positive stories for ocean acidification and underlines that sustainable management of seagrasses is critical to avoid their continued degradation and loss of carbon sequestration capacity.

Organotins (OTs) have caused widespread adverse effects on marine organisms, while they can also induce health problems to humans via consumption of contaminated seafood. This study aimed to quantify the tissue concentrations of OTs in 11 seafood species in Hong Kong, and assess the human health risk for consuming these species. The tongue sole Paraplagusia blochii had the highest concentration of total OTs. Triphenyltin (TPT) accounted for 56–97% of total OTs. The highest hazard quotient (HQ) for TPT was 1.41 in P. blochii, while the HQs for butyltins were much less than 1. The results indicated that it is likely to have certain health risks for consuming P. blochii due to its high TPT contamination. Therefore, TPT should be a priority pollutant of concern. Appropriate management actions should be taken to control its use and release in the region in order to safeguard the marine ecosystem and human health.

Little attention is given to applying the artificial neural network (ANN) modeling technique to understand site–specific air pollution dispersion mechanisms, the order of importance of meteorological variables in determining concentrations as well as the important time scales that influence emission patterns. In this paper, we propose a methodology for extracting the key information from routinely–available meteorological parameters and the emission pattern of sources present throughout the year (e.g. traffic emissions) to build a reliable and physically–based ANN air pollution forecasting tool. The methodology is tested by modeling NO2 concentrations at a site near a major highway in Auckland, New Zealand. The basic model consists of an ANN model for predicting NO2 concentrations using eight predictor variables: wind speed, wind direction, solar radiation, temperature, relative humidity, as well as “hour of the day”, “day of the week” and “month of the year” representing the time variations in emissions according to their corresponding time scales. Of the three input optimization techniques explored in this study, namely a genetic algorithm, forward selection, and backward elimination, the genetic algorithm technique gave predictions resulting in the smallest mean absolute error. The nature of the internal nonlinear function of the trained genetically–optimized neural network model was then extracted based on the response of the model to perturbations to individual predictor variables through sensitivity analyses. A simplified model, based on the successive removal of the least significant meteorological predictor variables, was then developed until subsequent removal resulted in a significant decrease in model performance. The developed ANN model was found to outperform a linear regression model based on the same input parameters. The proposed approach illustrates how the ANN modeling technique can be used to identify the key meteorological variables required to adequately capture the temporal variability in air pollution concentrations for a specific scenario.

Source apportionment study of aerosols over Bay of Bengal (BOB) were investigated during Integrated Campaign on Aerosol Radiation Budget (ICARB) in the pre–monsoon (March–April 2006) and winter (December–January 2008–09) seasons. Positive matrix factorization (PMF) was applied to identify sources of ambient particulate matter using daily chemical composition data collected in the pre–monsoon (total suspended particles, TSP) and winter season (particles with a diameter < 10μm, PM10). Sea salt (SS), secondary aerosol (SA), Si–dust, fossil fuel combustion (FFC), biomass burning (BB) sources have been identified in both seasons, however their relative contributions were different. The combined contribution of Si–dust, secondary aerosol and fossil fuel combustion, constitute ~67% of particulate matter in pre–monsoon, whereas, secondary aerosols and biomass burning were the major contributors (63.2%) to particulate matter in winter. The identified sources effectively predict the measured particulate concentration in the pre–monsoon (r2=0.74) and winter season (r2=0.82). Another receptor model, principal component analysis (PCA) was done to increase the plausibility of the results obtained by PMF. PCA resulted in the identification of the sources that were comparable to the PMF outputs. PCA of TSP in the pre–monsoon season resulted in the extraction of three components (crustal dust + secondary aerosol, biomass burning, fossil fuel combustion + industrial emissions) that explained the 83% of the variance in the data. Similarly, in winter season, PCA resulted in the extraction of four components (biomass burning + secondary aerosol, industrial emission, crustal dust, sea salt) that explained the 86% of the variance of the data.

A laboratory study was performed to investigate the composition of products formed from OH–initiated oxidation of aromatic hydrocarbon 1,3,5–trimethylbenzene. The experiments were conducted by irradiating 1,3,5–trimethylbenzene/CH3ONO/NO/air mixtures in smog chamber. The chemical composition of gas and particle–phase products have been investigated with gas chromatography/mass spectrometry (GC/MS) and the aerosol laser time–of–flight mass spectrometer (ALTOFMS), respectively. Experimental results showed that 3,5–dimethyl benzaldehyde, 2,4,6–trimethylphenol, 2–methyl–4–oxo–2–pentenal and 3,5–dimethyl–2–furanone were the predominant products in both the gas and particle–phases. However, there were some differences between detected gas–phase products and those of particle–phase, for example, oxalic acid, 2–methyl–2–hydroxy–3,4–dioxo–pentanal, and 2,3,5–trimethyl–3–nitro–phenol were only existing in the particle–phase. The possible reaction mechanisms leading to these products are also proposed. Compared to offline methods such as GC–MS measurement, the ALTOFMS detection can analyze real–time the secondary organic aerosol (SOA) successfully and provide more information on the products. Thus, ALTOFMS is a useful tool to reveal the formation and transformation processes of SOA particles in smog chambers.