Day 2 :
Ben-Gurion University of the Negev, Israel
Keynote: Modeling of air pollutants dispersion from industrial sources in environmental impact assessment
Time : 09:30-10:20
Boris Krasovitov currently working at the Mechanical engineering department of Ben-Gurion University of the Negev in Israel has more than twenty years of experience in physics of aerosols, air quality and air pollution control. His research focuses on air pollution modeling and scavenging of polluted aerosols and gases from the Atmosphere.
Although it is commonly accepted that air pollution is dominated by local emissions many studies report that plumes of harmful pollutants can be transported by wind across oceans and continents and warn about the growing danger of air quality degradation. Air pollutants released from industrial sources in a city may have a significant impact on human health, depending on the properties and atmospheric lifetime of the pollutants. The International Agency for Research on Cancer (IARC) evaluation showed an increasing risk for a wide range of diseases, e.g. lung cancer, respiratory and heart diseases, with increasing levels of exposure to particulate matter and air pollution (IARC, 2013). Adsorption of trace atmospheric gases such as NO2, SO2, and CO2 by carbon based aerosol particles emitted from industrial sources contributes to the evolution of concentration distribution of the trace constituents and can affect the subsequent chemical reactions in the atmosphere. In this connection, it is essential to evaluate the air quality levels of the atmosphere in order to assess the possible health impact of air pollutants. Clearly, modeling of air pollutants dispersion and deposition, in combination with air quality monitoring, are essential and complementary tools for long and short term air pollution control strategies.
In the framework of our study, we developed new approaches for urban and regional air pollution modeling, wet and dry deposition of particulate matter and adsorption of trace atmospheric gases by carbon based aerosol particles emitted from industrial sources. The developed models are used to predict the impacts of emission controls on the atmospheric concentrations and deposition of gaseous pollutants, fine and coarse particulate matter (PM2.5–10) and other air pollutants. The assessments of human exposure to various contaminants are based on contaminant concentration and on the parameters related with the exposure event, e.g. characteristics of the atmospheric boundary layer, precipitation rate etc. The obtained results can be useful in the analysis of different meteorology–chemistry models including scavenging of aerosols in air pollution plumes by rain and for the assessment of human exposure to various contaminants including particulate matter and hazardous gases emitted from industrial sources.
South African weather service, South Africa
Keynote: Climatic variables and the recent spike in malaria morbidity and mortality in mutale municipality, south Africa: An 18-year data analysis
Time : 10:20-11:10
Abiodun Adeola works as a lead scientist: climate change and variability in the research unit of South African Weather Service. His particular research interest is climate, climate change and variability impacts on heath. He is proficient in the application of remote sensing and geographic information system in providing solutions to environmental health problems through climate change analysis and modelling. He has a strong passion in improving the health and wellbeing. As part of his PhD research, he has developed a SARIMA model using remotely derived environmental variables to predict malaria cases in South Africa. Article of the model is under review with Eco Health journal. He is currently a leading member of a research collaboration group on “Developing an integrated modeling and surveillance system based on climate, land use, and malaria transmission dynamics in the eastern Limpopo river valley, South Africa.
Statement of the Problem: The malaria control program community of South Africa, received a seemly blow as an awakening call on the reality of the country’s target of year 2018 to eliminate malaria. The north-eastern part of the country comprising of Limpopo, Mpumalanga and KwaZulu-Natal have recorded a sudden rise in the number of malaria morbidity and mortality in the current malaria season. This paper aims at retrospectively and prospectively exploring the impact of climate variability among other factors driving the persistent transmission of malaria in Mutale, Limpopo Province of South Africa.
Methodology & Theoretical Orientation: A time series and multivariate analysis was performed on monthly total rainfall, monthly mean maximum and minimum temperature and monthly case data of malaria in Mutale municipality for the period of 2000 to 2017. The Rossby centre regional atmospheric model, (RCA4 RCM) was used to perform climate analysis and projections for rainfall and near-surface (2m) temperature. Findings: The time series analysis indicated that an average of 629.5mm of rainfall was received over the period of study. The rainfall has an annual variation of about 0.46%. Both maximum and minimum temperature showed a positive increasing trend in their mean. Spearman’s correlation analysis indicated that all climatic variables are positively correlated with malaria morbidity. Further analysis revealed that total monthly rainfall and monthly minimum temperature, with one month lagged effect were the most significant climatic variable influencing malaria transmission. More particularly, malaria morbidity showed a strong relationship with episode of rainfall above 800 mm and above 5-year running mean of rainfall. Furthermore, the RCA4 model indicated that, annual rainfall in the province will be 0% - 15% drier (below average) and seasonally, the western part of the province will be 5% wetter in December – February (DJF) and 5% dryer in the eastern part in March – May (MAM), June – August (JJA) and <20% dryer in September – November (SON). Near-surface temperature is projected to increase between +1.5°C - +2.5°C in 29-year period.
Conclusion & Significance: Adequate understanding of climatic variables dynamics retrospectively and prospectively is imperative in seeking answers to malaria morbidity among other factors, particularly in the wake of the sudden spike of the disease in the province.