Increased human activity and consumption of natural energy resources have led to decline in fossil fuel. These current methods of energy production are not compatible with the environment. In this study catalyst-less and mediator-less membrane microbial fuel cell (CAML-MMFC) represents a new method for simultaneous dairy industry wastewater treatment and bioelectricity generation. The CAML-MMFC used was designed as two chambered that included an anaerobic anode and aerobic cathode compartment and was separated from each other by a proton exchange membrane. The anode and cathode electrodes were made from graphite plate. Current intensity, power density and voltage produced from wastewater as fuel were measured and the effluent from the anode compartment was examined to evaluate pollutant decrease. The maximum current intensity and power density produced were respectively 3.74mA and 621.13mW/m2 on the anode surface, at OLR equal to 53.22kgCOD/m3d and at the external resistance of 1kΩ. The maximum voltage produced was 0.856V at OLR equal to 53.22kgCOD/m3d and at temperature 35oC. The maximum coulombic efficiency of 37.16% was achieved at OLR equal to 17.74kgCOD/m3d. The HRT was examined as a factor influencing the power generation and when it was 5 day, maximum voltage and power density were obtained. The maximum removal efficiency of COD, BOD5, NH3, NH4+, dissolved phosphorus, phosphorus in suspended solids, SO42−, TSS, and VSS was respectively achieved at 90.46%, 81.72%, 73.22%, 69.43%, 31.18%, 72.45%, 39.43%, 70.17% and 64.6%. The results showed that generating bioelectricity and dairy industry wastewater treatment by CAML-MMFC are a good alternative for producing energy and treating wastewater at the same time

Some epidemiological evidence has shown a relation between ambient air pollution and adverse health outcomes. The aim of this study was to investigate the effect of air pollution on mortality from respiratory diseases in Tehran, Iran. In this ecological study, air pollution data was inquired from the Tehran Province Environmental Protection Agency and the Tehran Air Quality Control Company. Meteorological data was collected from the Tehran Meteorology Organization and mortality data from the Tehran Cemetery Mortality Registration. Generalized Additive Models (GAM) was used for data analysis with different lags, up to 15 days. A 10-unit increase in all pollutants except CO (1-unit) was used to compute the Relative Risk of deaths. During 2005 until 2014, 37,967 respiratory deaths occurred in Tehran in which 21,913 (57.7%) were male. The strongest relationship between NO2 and PM10and respiratory death was seen on the same day (lag 0), and was respectively (RR = 1.04, 95% CI: 1.02–1.07) and (RR = 1.03, 95% CI: 1.02–1.04). O3 and PM2.5 had the strongest relationship with respiratory deaths on lag 2 and 1 respectively, and the RR was equal to 1.03, 95% CI: 1.01–1.05 and 1.06, 95% CI: 1.02–1.10 respectively. NO2, O3, PM10 and PM2.5 also showed significant relations with respiratory deaths in the older age groups. The findings of this study showed that O3, NO2, PM10 and PM2.5 air pollutants were related to respiratory deaths in Tehran. Reducing ambient air pollution can save lives in Tehran.

Malaria is among the most important parasitic diseases, and is one of the endemic diseases in Iran. This disease is often known as a disease related to climate changes. Due to the health and economic burden of malaria and the location of Kerman province in an area with high incidence of malaria, the present study aimed to evaluate the effects of climatic factors on the incidence of this disease. Data on the incidence of malaria in Kerman province was inquired from Kerman and Jiroft Medical Universities and climatic variables were inquired from the meteorological organization of Kerman. The data was analyzed monthly from 2000 to 2012. Variations in incidence of malaria with climatic factors were assessed with negative binomial regression model in STATA11software. In order to determine the delayed effects of meteorological variables on malaria incidence, cross-correlation analysis was done with Minitab16. The most effective meteorological factor on the incidence of malaria was temperature. As the mean, maximum, and minimum of monthly temperature increased, the incidence rate raised significantly. The multivariate negative binomial regression model indicates that a 1 °C increase in maximum temperature in a given month was related to a 15% and 19% increase on malaria incidence on the same and subsequent month, respectively (p-value = 0.001). Humidity and Rainfall were not significant in the adjusted model. Temperature is among the effective climatic parameters on the incidence of malaria which should be considered in planning for control and prevention of the disease.