Spatial and Temporal Trends of PM₁₀ and SO₂ in the Richards Bay Area

Air pollution is a public health emergency (WHO, 2016a). It is the biggest environmental risk to health with a global responsibility for about 1 in 9 deaths annually. In 2013, Statistics SA indicated that 10 % of all deaths in South Africa were attributed to respiratory diseases. Areas with increased industrial growth such as Richards Bay are particularly vulnerable. The paper analyses the spatial and temporal concentration trends of PM₁₀ and SO₂ in Richards Bay over the last two decades; specifically, since the promulgation of national ambient air quality standards (NAAQS) and minimum emissions standards (MES). Mann-Kendell trend tests was applied to monitoring data from the Richards Bay Clean Air Association (RBCAA) to test for an improving or worsening trend and the significance thereof. The pollution concentration data was also compared to NAAQS and the WHO annual guidelines. Overall, results indicate that although there has been a downward trend in the concentration of PM₁₀ and SO₂ emissions in the study area, the trends are not statistically significant. However, there was indication of statistically significant reducing trends in ambient concentrations at some stations. The concentrations at all stations were below NAAQS

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

Changes in health risk associated with air pollution and policy response effectiveness, Richards Bay, South Africa

Lung and bronchus cancer, asthma, acute lower respiratory infections (ALRI), ischemic heart diseases (IHD), cerebrovascular diseases (CEV) are disorders that have been widely associated with air pollution. More so, research shows that more than 5.5 million people die prematurely every year due to household and outdoor air pollution placing it as the fourth highest-ranking risk factor for death globally (Forouzanfar et al., 2015). Setting a minimum emission standard for industrial sources is a way to control air pollution and to minimize adverse impacts on people. With an aim to ascertain pollution policy intervention effectiveness, this study uses the case of Richards Bay to determine changes in health risk associated with air quality pollution exposure and the benefits of policy intervention. The study looks at trends of mortality in the last 20 years, the change in the ranking of 6 air-related mortality causes and Year of Life Lost (YLL) as a result of pollution. Results indicate a 24% decrease in the YLL due to air quality related diseases since 2009 when minimum emission standards were promulgated. The decrease can be observed across all age groups except for the 15-24-year-old, where cases of asthma and acute lower respiratory infections (ALRI) are the major mortality drivers. The adults and the older generation are now living slightly longer, although cases of CEV in that generation as well as the younger generation is still an issue that requires continuous monitoring and intervention. The study concludes that there is an improvement that could be attributed to policy implementation. However, the increase in mortality due to certain disease cases such as cancer of the bronchus and lung whose onset could be prior to 2010 signifies that the pollution control efforts need to continue and be stepped up. The increase of ALRI, which adversely affects children, is of concer

Public perceptions of air quality status and suggestions for improvement: The case of Richards Bay and its surroundings, uMhlathuze Local Municipality, South Africa

Whereas industrial growth is instrumental in unlocking poverty and advancing development, often, the effect of pollution on the environment, particularly air quality, is seldom accurately predicted. The effects, which include mortality, morbidity, and loss of productive time, are demonstrated later after the damage is done. The views of the pollution-exposed public in industrialised centres is important to ascertain if policy intervention is enhancing environmental protection for all and justice by extension. Through an online survey, 215 residents of the rapidly industrialising Richards Bay and surrounding areas in South Africa responded to the questions about their perceptions of air quality and recommendations to improve air quality management. Results indicate a concern over air quality with most residents perceiving the air quality as fair or poor. Industrial emission was cited as the leading cause of pollution followed by sugar cane and agrarian burning. Irritation of the ear, nose and throat, as well as sneezing and coughing, were the health effects experienced by residents for which air pollution can be partly attributed. The public recommends an improvement in air quality monitoring, consequence management, technology and public transport system. In addition, they recommended the introduction of air quality offsets, incentives schemes, more public involvement, coordinated planning and better collaboration as a recipe for success in air quality management

Application of deep learning with stratified K-fold for vegetation species discrimation in a protected mountainous region using Sentinel-2 image

Understanding the spatial distribution of vegetation species is essential to gain knowledge on the recovery process of an ecosystem. Few studies have used deep learning and machine learning models for image processing focusing on forest/crop classification. This study, therefore, makes use of a multi-layer perceptron (MLP) deep neural network to discriminate grass species in a mountainous region using Sentinel-2 images. Vegetation indices, Sentinel-1 and ASTER DEM were combined with Sentinel-2 images to improve classification accuracy. Stratified K-fold was used to ensure balanced training and test data. The results, when compared with other commonly used machine learning models, outperformed them all. It produced a better discriminate of the grass species when ASTER DEM was combined with Sentinel-2 images, with overall F1 score of 92%. The results of the species discrimination show a general increase in increaser II species such as Eragrostis curvula and a decrease in decreaser species like Phragmites australis

Variation in Isolate Virulence and Accession Resistance Associated with Diaporthe aspalathi, D. caulivora, and D. longicolla in Soybean

Important stem and grain diseases of soybean (Glycine max [L.] Merr.) caused by Diaportheaspalathi, D. caulivora, and D. longicolla reduce yield in the United States. Sources of resistance to these pathogens have previously been reported; however, there is limited information regarding their resistance when exposed to geographically distinct isolates of the same species. In this study, four accessions from the USDA Soybean Germplasm Collection with reported resistance to D. aspalathi, D. caulivora, or D. longicolla were evaluated using geographically representative isolates within each species from the United States. For each fungus, a greenhouse experiment was conducted as a completely randomized design with a factorial arrangement (isolate rif accession). Plants were inoculated at the second to third of each Diaporthe species. Pathogenicity was assessed 21 days postinoculation as 0 = no lesion, 0.5 = lesion length?\u3e 1 cm, and 1 = dead plant. A significant isolate-by-accession interaction (P \u3c 0.05) was observed to affect pathogenicity as analyzed using nonparametric statistics (relative treatment effects [RTEs]), indicating that accessions responded differently to the isolates. Correlation analyses suggested that the RTEs on ‘Tracy-M’, ‘Dowling’, and ‘Crockett’ were weakly to moderately correlated with those of the D. aspalathi-susceptible ‘Bragg’, as well as for ‘PI567473B’ and ‘Century’ (D. caulivora), and ‘PI417507’ (D. longicolla), with the RTEs on ‘Hawkeye’ (P?\u3e 0.05) indicating possible genetic variation for resistance within these accessions. Our results provide information related to the resistance of previously identified accessions to develop commercial cultivars with resistance to important pathogens within the genus Diaporthe