Effects of rainfall characteristics on runoff quality parameters within an industrial sector in Tennessee, USA.

The relationship between rainfall characteristics and pollutant discharge has rarely been investigated in industrial sectors. To address this need, we investigated the pollutant concentrations of surface runoff and the correlation between pollutant discharge and rainfall characteristics using the self-reported stormwater quality data collected under the Tennessee Multi-Sector Permit program for two industrial facilities in West Tennessee. The variation of certain stormwater quality parameters over this period was utilized as an indicator to evaluate the effectiveness of control measures implemented at these two facilities. Furthermore, the Water Quality Index (WQI) as an indicator to assess the temporal changes in stormwater quality at industrial facilities was determined using the Weighted Sum (WSM) and Canadian Council of Ministers of the Environment (CCME) methods. The principal component analysis (PCA) and Pearson correlation coefficient were utilized to understand the correlation between runoff quality parameters, rainfall characteristics, and the sources of pollutants. The results demonstrated lower WQI indices using the WSM method compared to the CCME method. The data analysis revealed that 93.1%, 100%, 86.2%, and 48.3% of Al, Mg, Cu, and Fe experienced a concentration greater than the benchmark level, respectively. There was a significant relationship between Total suspended solids (TSS) and Al, Chemical Oxygen Demand (COD), Fe, oil and grease (O&G), and Zn concentrations. As a result, TSS could be a priority pollutant for designing various best management practices (BMPs) and low impact developments (LIDs). The result of the PCA and Pearson correlation coefficient showed that Al concentration made a significant correlation with the rainfall depth and rainfall duration. This analysis also illustrated that biochemical oxygen demand (BOD5), COD, and O&G concentrations were highly correlated with antecedent dry days (ADDs). However, pH was more related to rainfall depth and rainfall intensity. This study informs both regulatory agencies and industry stakeholders regarding the importance of evaluating self-reported stormwater quality data.

Morphometric differentiation between camel and sheep strains of Echinococcus granulosus using computer image analysis system (CIAS)

OBJECTIVE@#To find importance of morphometric criterion of larval rostellar hook of Echinococcus granulosus (E. granulosus) and the easy and reliable method for distinguish sheep and camel strains in epidemiologic studies.@*METHODS@#Larval rostellar hooks (n=1860) of 31 camel and sheep isolates in Iran, which already had been characterized by PCR, were carefully processed by computerized imagime analysis system (CIAS) and acquired data about rostellar hooks were analyzed using software SPSS.@*RESULTS@#Measurement analysis of rostellar hooks [mean length (24.23±3.12) μ m] indicated that length of the large hook was a remarkable parameter for strain differentiation. Data analysis demonstrated that CIAS could be used as a reliable tool to distinguish camel from sheep strains with high sensitivity (95.2%) and specificity (91.5%).@*CONCLUSIONS@#CIAS as a specific, sensitive, economic, fast, and reliable means might be used for differentiation of E. granulosus strains. Although perimeter and area were measured by digital technology, they were not shown as discriminative criterion as total hook length did.