ABSTRACT
Hydrocarbons, originating from oil and gas industries, are considered a potential risk for Nayband Bay, a natural marine park with extended mangroves, located on the north coastlines of the Persian Gulf, Iran. This paper determines the potential sources and spatial distribution of hydrocarbons, especially aliphatic hydrocarbons (AHCs), in Nayband Bay through the simultaneous application of three indices in the coastline surface sediments. To this end, a field study was conducted in the inter-tidal coastal zones and wetlands. Sediment samples were taken from surface layers along four transects with four sampling points at different distances from the gulf. The hydrocarbon compounds of the samples including AHCs, total petroleum hydrocarbons, and heavy metals (Ni, V as crude oil indicators) were analyzed and classified to discover the pollution indicators. Pearson pairwise correlation and cluster analyses along with pollution indices were employed to describe the spatial distribution pattern of hydrocarbons, identify hot spots, and determine the potential origin of AHCs. Different interpolation scenarios based on topographic and oceanic features were proposed to detect the spatial dynamics of AHCs. The results revealed that hydrocarbons mainly originated from anthropogenic sources including oil and gas industries located far from the affected area. It was also concluded that the long-distance pollution transfer was based on oceanic currents and wind direction in the bay. The proposed scenarios showed that the mean concentration values of total organic carbon and total organic material vary in the range 0.19 ppm to 0.4 ppm and 2.88 ppm to 3.20 ppm, respectively.
ABSTRACT
To date, few studies have investigated the simultaneous effects of macro-scale parameters (MSPs) such as land use, population density, geology, and erosion layers on micro-scale water quality variables (MSWQVs). This research focused on an evaluation of the relationship between MSPs and MSWQVs in the Siminehrood River Basin, Iran. In addition, we investigated the importance of water particle travel time (hydrological distance) on this relationship. The MSWQVs included 13 physicochemical and biochemical parameters observed at 15 stations during three seasons. Primary screening was performed by utilizing three multivariate statistical analyses (Pearson's correlation, cluster and discriminant analyses) in seven series of observed data. These series included three separate seasonal data, three two-season data, and aggregated three-season data for investigation of relationships between MSPs and MSWQVs. Coupled data (pairs of MSWQVs and MSPs) repeated in at least two out of three statistical analyses were selected for final screening. The primary screening results demonstrated significant relationships between land use and phosphorus, total solids and turbidity, erosion levels and electrical conductivity, and erosion and total solids. Furthermore, water particle travel time effects were considered through three geographical pattern definitions of distance for each MSP by using two weighting methods. To find effective MSP factors on MSWQVs, a multivariate linear regression analysis was employed. Then, preliminary equations that estimated MSWQVs were developed. The preliminary equations were modified to adaptive equations to obtain the final models. The final models indicated that a new metric, referred to as hydrological distance, provided better MSWQV estimation and water quality prediction compared to the National Sanitation Foundation Water Quality Index.
