Seasonal dynamics of phytoplankton in response to environmental variables in contrasting coastal ecosystems

Environmental Science and Pollution Research - Seasonal distribution of phytoplankton community and size structure was assessed in three different tropical ecosystems of the western Bay of Bengal...     

Green method of stemming the tide of invasive marine and freshwater organisms by natural filtration of shipping ballast water

Environmental Science and Pollution Research - Marine and freshwater pollution caused by transport of invasive species in shipping ballast water is a major global problem and will increase in...     

Development and evaluation of air pollution–linked quality of life (AP-QOL) questionnaire: insight from two different cohorts

Environmental Science and Pollution Research - In this study, the air pollution–related quality of life (AP-QOL) questionnaire was carried out in two geographically and economically different...     

Source apportionment of fine particles in Tennessee using a source-oriented model

Source apportionment of fine particles (PM2.5, particulate matter < 2 microm in aerodynamic diameter) is important to identify the source categories that are responsible for the concentrations observed at a particular receptor. Although receptor models have been used to do source apportionment, they do not fully take into account the chemical reactions (including photochemical reactions) involved in the formation of secondary fine particles. Secondary fine particles are formed from photochemical and other reactions involving precursor gases, such as sulfur dioxide, oxides of nitrogen, ammonia, and volatile organic compounds. This paper presents the results of modeling work aimed at developing a source apportionment of primary and secondary PM2.5. On-road mobile source and point source inventories for the state of Tennessee were estimated and compiled. The national emissions inventory for the year 1999 was used for the other states. U.S. Environmental Protection Agency Models3/Community Multi-Scale Air Quality modeling system was used for the photochemical/secondary particulate matter modeling. The modeling domain consisted of a nested 36-12-4-km domain. The 4-km domain covered the entire state of Tennessee. The episode chosen for the modeling runs was August 29 to September 9, 1999. This paper presents the approach used and the results from the modeling and attempts to quantify the contribution of major source categories, such as the on-road mobile sources (including the fugitive dust component) and coal-fired power plants, to observed PM2.5 concentrations in Tennessee. The results of this work will be helpful in policy issues targeted at designing control strategies to meet the PM2.5 National Ambient Air Quality Standards in Tennessee.     

Power asymmetries and institutions: landscape conservation in central India

Understanding the importance of cross-sectoral implications of climate and socio-economic change in Scotland is essential for adaptation policy. This study explored the direct and indirect sectoral impacts of future change using the CLIMSAVE Integrated Assessment Platform. There is great spatial diversity in projected impacts across Scotland, and increasing uncertainty in the direction of change of impacts from the national to regional scale associated with climate uncertainty. Further uncertainty associated with socio-economic change results in 6 out of 13 indicators (artificial surfaces, biodiversity vulnerability, forest area, land-use intensity, irrigation usage and land-use diversity) with robust directions of change at the national scale and only three (artificial surfaces, forest area and irrigation usage) that are robust across all regions of Scotland. Complex interactions between socio-economic scenario assumptions (e.g. food imports, population and GDP), climatic suitability and agricultural productivity and profitability lead to significant national and regional changes in the distribution and extent of land cover types, with resultant cross-sectoral interactions with water, forestry and biodiversity. Consequently, stakeholders characterised robust adaptation policy options, within the CLIMSAVE participatory process, as those beneficial to society (and the country) in all scenarios, irrespective of the direction of change of the impacts. The integration in CLIMSAVE of a participatory scenario development process and an integrated participatory modelling framework has allowed the exploration of future uncertainty in a structured approach and better represented the importance of qualitative information and the social and institutional contexts within adaptation research.     

Coupling fishery dynamics,human health and social learning in a model of fish-borne pollution exposure

Pollution-induced illnesses are caused by toxicants that result from human activity and are often entirely preventable. However, where industrial priorities have undermined responsible governance, exposed populations must reduce their exposure by resorting to voluntary protective measures and demanding emissions abatement. This paper presents a coupled human–environment system model that represents the effects of water pollution on the health and livelihood of a fishing community. The model is motivated by an incident from 1949 to 1968 in Minamata, Japan, where methylmercury effluent from a local factory poisoned fish populations and humans who ate them. We model the critical role of risk perception in driving both social learning and the protective feedbacks against pollution exposure. These feedbacks are undermined in the presence of social misperceptions such as stigmatization of the injured. Through numerical simulation and scenario analysis, we compare our model results with historical datasets from Minamata, and find that the conditions for an ongoing pollution epidemic are highly unlikely without social misperception. We also find trade-offs between human health outcomes, the viability of the polluting industry and the survival of the fishery. We conclude that an understanding of human–environment interactions and misperception effects is highly relevant to the resolution of contemporary pollution problems, and merits further study.     

Assessing Respiratory Morbidity Through Pollution Status and Meteorological Conditions for Delhi

The study focuses on assessing the status of respiratory morbidity in Delhi over a four years period from 2000–2003. An attempt was made to investigate the role of important pollutants (SO₂, NO₂, SPM and RSPM) and various meteorological factors (temperature minimum & maximum, relative humidity at 0830 and 1730 hrs. and wind speed) in being responsible for respiratory admissions on account of COPD, asthma and emphysema. The study showed that winter months had greater exposure risk as pollutants often get trapped in the lower layers of atmosphere resulting in high concentrations. Statistical analysis revealed that two pollutants have significant positive correlation with the number of COPD cases viz., SPM (r = 0.474; p < 0.01) and RSPM (r = 0.353; p < 0.05), while a meteorological factor temperature (minimum) has a significant negative correlation (r = −0.318; p < 0.05) with COPD. Stepwise multiple regression analysis was performed for COPD as dependent variable and R² value of 0.33 was obtained indicating that SPM and RH(1730) were able to explain 33 percent variability in COPD. The partial correlation of SPM and RH(1730) on COPD was higher than any other combination and therefore they can be regarded as important contributing variables on COPD.