Bacterial communities in the polluted area of pyrite tailings: From the upstream, pollutant source, and to the downstream

Pyrite tailings can cause serious pollution to the surface water as the strong acidity, high iron and sulfate concentration in the leachate. The bacterial communities of pyrite tailings polluted area were still unclear which could restrict the recognition of the pyrite tailings pollution effect and further impede the development of microbial or ecology treatment technologies. In this study, the bacterial communities in the polluted area of pyrite tailings, from the upstream, pollutant source, and to the downstream, were analyzed with Illumina HiSeq sequencing. Results showed that Acinetobacter and Flavobacterium were abundant in the water and sediment of upstream and downstream while Bacteroides, Lactobacillus, and Akkermansia were abundant in the pollutant source. Sulfur-metabolizing or iron-metabolizing bacteria extensively existed in the polluted area in which Acidiferrobacter, Ferrithrix, and Desulfovibrio played crucial roles on the whole communities. Sulfur-metabolizing bacteria (e.g. Thiomonas, Sulfurospirillum, and Desulfobulbus) and iron-metabolizing bacteria (e.g. Ferrimicrobium, Ferrithrix, and Ferrovum) were introduced to the river polluted by pyrite tailings. Pyrite tailings can remarkably change the physicochemical characteristics and bacterial communities of river water and sediment.

Rancang Bangun Green Belt Untuk Pengendalian Pencemaran Debu di Kawasan Industri Terboyo (Jalan Kaligawe)

Pencemaran udara adalah masuknya atau bercampurnya unsur-unsur berbahaya ke atmosfer yang dapat menyebabkan kerusakan lingkungan, gangguan kesehatan manusia pada umumnya, dan penurunan kualitas lingkungan. Salah satu solusi untuk mengatasi masalah pencemaran udara adalah dengan merencanakan Green Belt. Green Belt atau Sabuk hijau adalah kawasan bebas bangunan atau ruang terbuka hijau di sekitar kawasan sumber pencemar yang berguna sebagai penyaring fisik pencemar udara serta aspek lain seperti estetika, fungsi peneduh dan penunjang keanekaragaman hayati. Oleh karena itu, perencanaan Green Belt menjadi penting sebagai aspek yang dapat mengendalikan tingkat pencemaran udara, khususnya pencemaran debu, pada lokasi perencanaan yang ditargetkan, khususnya Kawasan Industri Terboyo. Berdasarkan sampling yang dilakukan, angka konsentrasi debu menunjukkan angka yang cukup tinggi yaitu 801,6 mg/m3. Dengan desain Green Belt jenis pohon Acacia mampu menurunkan konsentrasi debu mulai dari efisiensi 15,84% pada tahun tanam dan meningkat pesat setiap tahunnya. Efisiensi optimal Green Belt akan tercapai pada tahun ke-2 dengan laju 71,40% dan akan mampu mencapai efisiensi maksimum pada tahun ke-5 dengan laju 87,92%. ABSTRACTAir pollution is the entry or mixing of hazardous elements into the atmosphere which can cause environmental damage, disturbances to human health in general and reduce environmental quality. One of the solutions to tackle air pollution problems is to plan a Green Belt. Green belt is a building-free zone or green open space around the pollutant source area which is useful as a physical filter for air pollutants as well as other aspects such as aesthetics, shading functions, and biodiversity support. Therefore, planning a Green Belt is important as an aspect that can control the level of air pollution, especially dust pollution, at the targeted planning location, especially Terboyo Industrial Area. Based on the sampling carried out, the dust concentration figure shows a high number, namely 801.6 mg / m3. With the Acacia tree species Green Belt design, it can reduce dust concentrations starting from an efficiency of 15.84% in the planting year and increasing rapidly each year. The optimum efficiency of the Green Belt will be achieved in the 2nd year with the rate of 71.40% and it will be able to reach the maximum efficiency in the 5th year with the rate of 87.92%.

Assessment of organic carbon contamination in the unsaturated zone: a case of Yogyakarta City, Indonesia

In 1997, groundwater pollution was caused by a diesel leak at the Yogyakarta City Railway Station. People in the south of the railway station discovered the presence of diesel in dug wells in 2001. The existing diesel is still found in dug wells even though the pollutant source had been removed. The current source of pollution comes from diesel residues trapped in the unsaturated zone. Understanding the distribution and concentration of diesel in the unsaturated zone is the goal of this study. In this study, diesel concentration was measured based on Total Organic Carbon (TOC) levels. The research was conducted through shallow core and deep core drillings. Shallow core drilling was done at 14 points with a depth of 50 cm, and deep core drilling was done at nine drilling points with a depth of 15-17 m. 14 shallow core drilling samples were taken from a depth of 30 and 50 cm and nine deep core drilling samples were taken from a depth of 4-5 m and 10-11 m. The lithology logs in both drills were tested for diesel odour and TOC levels using the Soli TOC tool. Based on the test results, the smell of diesel was found at a depth of 10 to 15 m. TOC levels in the unsaturated zone ranged from 340 to 90,870 mg/L. TOC levels >30,000 mg/L were dominant at shallow depths even though they did not smell like diesel. At a location close to the source of the diesel tank leak at a depth of 4-5 m, the measured TOC level was 30,100 mg/L. The results showed that there were zones of high TOC levels associated with diesel odour layers. The zones existed because of the infiltration and percolation processes that had carried surface water and diesel pollutants and eventually moved horizontally following groundwater flow.

Environmental Cost in Indonesia Spillover Effect Between Consumption and Production

Reducing environmental costs is a significant concern for Indonesia's future. This paper explores Indonesia's environmental costs from emissions and forest resources and identifies the priority sectors in terms of economic and environmental performance. We use environmentally extended input–output analysis for calculating the environmental costs and further extension with linkages analysis to identify the priority sectors. The study finds that the total environmental costs of emissions due to final demand is around 7% of the GDP. This environmental cost is significantly due to domestic products with household consumption being the largest contributor. The top 10 sectors in the Indonesian economy are responsible for about 70% of the total environmental costs of emissions. Based on pollutant source, SOx, NOx, CO2, and CH4 contribute more than half of emissions' ecological costs. We also find that forest resources' environmental cost is only 7.5% of the total environmental cost. Last, this study finds that key sectors of economic and sustainability points of view are textile manufacturing; publishing, printing, and reproduction of recorded media; chemicals n.e.c.; manufacture of other non-metallic mineral products; construction; and other land transport. Finally, this paper discusses the policy options for Indonesia to promote sustainable consumption and production in terms of reducing environmental costs while managing economic development.

Atmospheric Contamination of Coastal Cities by the Exhaust Emissions of Docked Marine Vessels: The Case of Tromsø

Docked ships are a source of contamination for the city while they keep their engine working. Plume emissions from large boats can carry a number of pollutants to nearby cities causing a detrimental effect on the life quality and health of local citizens and ecosystems. A computational fluid dynamics model of the harbour area of Tromsø has been built in order to model the deposition of CO2 gas emitted by docked vessels within the city. The ground level distribution of the emitted gas has been obtained and the influence of the wind speed and direction, vessel chimney height, ambient temperature and exhaust gas temperature have been studied. The deposition range is found to be the largest when the wind speed is low. At high wind speeds, the deposition of pollutants along the wind direction is enhanced and spots of high pollutant concentration can be created. The simulation model is intended for the detailed study of the contamination in cities near the coast or an industrial pollutant source of any type of gas pollutant and can easily be extended for the study of particulate matter.

Atmospheric Contamination of Coastal Cities by the Exhaust Emissions of Docked Marine Vessels: The Case of Tromsø

Docked ships are a source of contamination for the city while they keep their engine working. Plumes emissions from large boats can carry a number of pollutants to the nearby cities causing a detrimental effect on the life quality and health of local citizens and ecosystems. A computational fluid dynamics model of the harbour area of Tromsø has been built in order to model the deposition of CO2 gas emitted by docked vessels within the city. The ground level distribution of the emitted gas has been obtained and the influence of the wind speed and direction, vessel chimney height, ambient temperature and exhaust gas temperature has been studied. The deposition range is found to be the largest when the wind speed is low. At high wind speeds, the deposition of pollutants along the wind direction is enhanced and spots of high pollutant concentration can be created. The simulation model is intended for the detailed study of the contamination in cities near the coast or an industrial pollutant source of any type of gas pollutants and can easily be extended for the study of particulate matter.

Using the Backward Probability Method in contaminant source identification with a finite-duration source loading in a river

Violation of industries in discharging their effluents into rivers leads to river pollution, which endangers the environment and human health. Appropriate tools are needed to deal with violations and protect rivers. The Backward Probability Method (BPM) is one of the most recommended tools identifying the release time and location of the pollutant source. However, the BPM generally was developed for groundwater and spill injection. Since most industries inject their effluents with a constant rate for a finite-duration, the use of prevailing models will have some errors. In this study, a numerical model was developed that could simulate a source with either a finite-duration or spill injection. This model is verified for two hypothetical cases and one real case. The results show that the model can accurately identify the release time and location of the pollutant source.

Identifying and Estimating the Location of Sources of Industrial Pollution in the Sewage Network

Harsh pollutants that are illegally disposed in the sewer network may spread beyond the sewer network—e.g., through leakages leading to groundwater reservoirs—and may also impair the correct operation of wastewater treatment plants. Consequently, such pollutants pose serious threats to water bodies, to the natural environment and, therefore, to all life. In this article, we focus on the problem of identifying a wastewater pollutant and localizing its source point in the wastewater network, given a time-series of wastewater measurements collected by sensors positioned across the sewer network. We provide a solution to the problem by solving two linked sub-problems. The first sub-problem concerns the detection and identification of the flowing pollutants in wastewater, i.e., assessing whether a given time-series corresponds to a contamination event and determining what the polluting substance caused it. This problem is solved using random forest classifiers. The second sub-problem relates to the estimation of the distance between the point of measurement and the pollutant source, when considering the outcome of substance identification sub-problem. The XGBoost algorithm is used to predict the distance from the source to the sensor. Both of the models are trained using simulated electrical conductivity and pH measurements of wastewater in sewers of a european city sub-catchment area. Our experiments show that: (a) resulting precision and recall values of the solution to the identification sub-problem can be both as high as 96%, and that (b) the median of the error that is obtained for the estimation of the source location sub-problem can be as low as 6.30 m.