Non-target effect of organic insecticides: effect of two plant extracts on soil microbial biomass and enzymatic activities in soil

Efficacious botanical derivatives can provide an alternative to synthetic pesticides for organic farming systems. However, there is lack of information regarding the side effects of organic pesticides on key soil ecological processes. In this study, we investigated the effects of aqueous extracts from Urginea maritima and Euphorbia myrsinites exhibiting translaminar and systemic activity against pests on microbial biomass and enzymatic activities in soil. Two grams of plant material was extracted with 100 ml of water and then diluted 1:100, 2:100, and 4:100 with distilled water. Diluted plant extracts were applied around hypocotyl of tomato by soil drench. The effect of both plant extracts on microbial biomass C, amount of total N and organic C, and enzymatic activity in soil was significant. After the last application, the highest microbial biomass C was determined in the lowest U. maritima concentration (U 1:100). Soils treated with the highest concentration of U. maritima (U 4:100) had always lower SMBC content than control soil. All concentrations of E. myrsinites decreased microbial biomass C by 18% to 27% compared to the control. Total nitrogen and organic carbon decreased in soils without (control) and with treated U. maritima extract from first application to last application. Phosphatase, urease, and β-glucosidase activities were monitored in plant extract-treated soils. Except U. maritima 1:100 treatments of second and fourth applications, the other treatments of plant extracts negatively affected enzymatic activity in soil. U. maritima and E. myrsinites plant extracts exhibited different effects on soil microbial biomass and activity, probably because of their different chemical contents.     

Prediction of ambient PM10 and toxic metals using artificial neural networks

In this study, an artificial neural network is employed to predict the concentration of ambient respirable particulate matter (PM10) and toxic metals observed in the city of Jaipur, India. A feed-forward network with a back-propagation learning algorithm is used to train the neural network the behavior of the data patterns. The meteorological variables of wind speed, wind direction, relative humidity, temperature, and time are taken as input to the network. The results indicate that the network is able to predict concentrations of PM10 and toxic metals quite accurately.     

Current and Future Strategies for Water and Wastewater Management of Istanbul City

Istanbul has experienced rapid increases in population to more than 12 million people, which has created infrastructure problems of water supply and wastewater treatment and disposal. In this article, the achievements and approaches of the Istanbul Water and Sewerage Administration (ISKI) to solve the water shortage problem and to improve services are summarized. Istanbul had a very severe water shortage problem in 1994 because of ignorance of the implementation of the needed projects. After reviewing the reasons and causes of the problem, new priority criteria adopted after 1994 are given. Following the implementation of the projects determined according to the aforementioned criteria, water supplied has exceeded the water demand. The added capacity is equal to one to three times of the capacity built up to 1994 for water treatment, service reservoirs, pumping stations, transmission lines, and the water distribution network; water quality has been improved the meet local and international potable water standards. Unaccounted for water has been reduced from 60% to 27%. The percentage of treated wastewater has been increased from 10% to 90% in 8 years, resulting in drastic improvements and rehabilitation of the Golden Horn and coastal water quality. Through improved customer services, complaints were reduced from 33% in 1994 to 0.3%. Some of the main criteria and the approaches behind this success are summarized. Published online     

Risk assessment for hazardous lubricants in machining industry

Knowing that over two-thirds of lubricant disposals return to the environment with no purification process, adequate strategies are demanded to reduce their risks. For this reason, the main focus of the present study is to describe an environment-friendly approach. In the first part, two widely used lubricants (mineral-based and vegetable-based) were introduced, and the reasons for their hazards were investigated. The composition of mineral-based lubricant was characterized by x-ray fluorescence elemental analysis. The result showed the presence of phosphorus, chlorine, and zinc dialkyl dithiophosphate in its composition that many scholars considered them to be the leading risk factor in the chemical composition of mineral-based lubricants. It has been focused on the potential risks of vegetable-based lubricants, which many researchers have identified as a safe lubricant. The Pseudomonas microbe was cultivated in the vegetable-based sample, and the result showed that although vegetable-based lubricants are compatible with humans, bacteria colony can quickly grow there without making any apparent changes that lead to harm to operators in a mysterious way. In the present work, the hypothesis of the safety of unreinforced vegetable-based lubricants has been rejected, and a new window on the environmental issues of vegetable-based lubricants has been presented. In the final, to eliminate environment-human risks and to reduce consumption of lubricant and natural resources to green manufacturing, a comprehensive study on the possibility of completely removing lubricant was performed. The results showed that the machining without lubricant could be replaced with the traditional method.

     

Nitrogen dioxide induced changes in level of free fatty acids, triglyceride, esterified fatty acid, ganglioside and lipase activity in the guinea pig brain.

The biochemical response to controlled inhalation of nitrogen dioxide (NO2) was studied in 18 male guinea pigs. Animals were exposed to 2.5, 5.0, and 10 ppm NO2 for 2h daily for 35 consecutive days, and the results compared with six control animals exposed to filtered air for 2h daily for same period. Five biochemical parameters, including triglyceride, free fatty acids, esterified fatty acid, ganglioside and lipase activity were measured immediately after the last day of exposure. At 2.5 ppm NO2 inhalation no significant changes occurred in any region of the central nervous system (CNS). While as the dose concentration was increased to 5 and 10 ppm nitrogen dioxide, significant dose-related alteration were observed in the levels of triglyceride, free fatty acid, esterified fatty acid, ganglioside and lipase activity in the different regions of the guinea pig CNS.     

Heavy metal mobility and potential availability in animal manure: using a sequential extraction procedure

In this study, dairy cow manure, goat manure, and chicken manure were collected from three farms and analyzed to find out the concentration of Cd, Co, Cr, Cu, Mn, Ni, Pb, and Zn. The concentration and potential of mobility and availability of heavy metals were studied in the animal manure samples. BCR Sequential extraction procedure was used to determine the binding forms of the metals. In this study, pseudo total concentrations of Mn and Zn were found out to be predominant in all the types of animal manure samples. According to the results, it was traced that Cr, Cu, and Ni were observed to be at the second highest level while Cd, Co, and Pb were seen at the lowest level in all the manure samples. When extractable amounts of heavy metals are taken into consideration, it is seen that the amount of the mobile fractions of heavy metals except for Cr and Ni are higher in comparison with that of immobile fraction in all the animal manure samples. It was also viewed that Mn, Cd, and Zn are more available in dairy cow manure and chicken manure whereas Cd, Co, and Mn are more available in goat manure.     

Analysis of periodic patterns of daily precipitation through simultaneous modeling of its serially observed occurrence and amount

Precipitation is of great importance to agriculture, environment and ecosystem as a regular precipitation pattern is usually vital to healthy plants; excessive or insufficient rainfall can be harmful. Periodic patterns of precipitation can be studied based on regularly observed data over time. Since regularly observed precipitation data are generally skewed with many zeros, two common analysis approaches have been proposed recently. One approach investigates precipitation using a two-part model where the occurrence and positive amount of precipitation are analyzed separately (Piantadosi et al. in Environ Model Assess 14:431–438, 2009), whereas the other approach handles occurrence and amount simultaneously using a Tweedie’s compound Poisson model for independent observations (Hasan and Dunn in Int J Climatol 32:1006–1017, 2012). The former approach fails to maintain the regular temporal structure of serially observed precipitation, whereas the latter approach ignores serial dependence. As there is generally substantial serial correlation in the observed sequence of precipitation data over time, we introduce a compound Poisson state-space model with serially correlated random effects for daily precipitation data. This approach characterizes both occurrence and amount of precipitation simultaneously while accounting for the corresponding serial correlation. Our main results depend only on the first- and second-moment assumptions of unobserved random effects. We illustrate our method with the analysis of the daily precipitation data recorded at Mount Washington, NH, USA.