Global NEST Journal

To assess the indoor air quality an investigation was conducted in Lucknow from November 2014 to October 2015. In total seven pollutants viz. SO2, NO2, CO2, CO, NH3, H2S, PM10 and PM2.5 were assessed in five urban and five rural houses. House selection was done after questionnaire survey with respiratory patients in two medical colleges. The average concentrations of PM10 (280 and 315 µg m-3) and PM2.5 (185 and 210 µg m-3) were highest in winter season. Excessive consumption of crude fuel to combat cold conditions was associated with high particulate concentrations in rural houses. Smoking was a common habit recognized indoors. Skin irritation was common during rainy season whereas complaints of cataract, cough and sneezing were prevalent in winters. Air quality index was calculated using three different methods and was poorest in winter season in rural houses particularly with values 716.1, 457.0 and 7.427 as calculated.

Wastewater from textile industry is considered one of the major environmental challenges due to the large volume of highly colored, polluted and toxic effluent. This study investigated the treatability of real textile wastewater by pilot-scale anoxic-aerobic Membrane Bioreactor (MBR) system without sludge wasting for operation period of 100 days. The proposed system was investigated under different Internal Recycle (IR) ratios and the impact of IR ratio on Total Organic Carbon (TOC), Total Nitrogen (TN) and Color removals were examined. Under IR ratios between anoxic and aerobic tanks of 0.0, 0.5 and 2.0, the respective average removal efficiency of TN was 20.9%,53.4% and 71.7%, whereas average color removal of 81%, 85% and 88%, respectively was noted. The results indicated that increase of recycle ratio from 0.5 to 2.0 enhanced TN removal to about 71% and color removal to above 85%. The IR between anoxic and aerobic tanks has a significant role in TN and color removal due its effect on the development of bacterial communities. On the other hand, the results indicate over 93% TOC removal, which was independent of IR ratio.

This paper analysis the contents and variation of heavy metals in wet and dry deposition in Changji (Xinjiang, China) revealed their reducing regularity for heavy metals in atmosphere in arid area. Samples (including 84 dry deposition samples and 16 wet deposition samples) were collected from January 2016 to December 2016, and the contents of heavy metals (Ni, Cu, Cd and Pb) were analyzed by AA-7000 atomic absorption spectrophotometer. The dry deposition fluxes of Ni, Cu, Cd and Pb are 3.70 mg/( m2.a), 4.81 mg/( m2. a), 0.53 mg/( m2•a) and 22.74 mg/( m2•a), respectively; the wet deposition fluxes of Ni, Cu, Cd and Pb are 0.77mg/( m2•a), 3.25mg/( m2•a), 0.04mg/( m2•a) and 0.11mg/( m2•a), respectively. Each of the four heavy metals deposition fluxes during heating period was higher than non-heating period, especially for Pb and Cd, which is mainly due to the emission of coal combustion for heating. During sampling periods, the ratio of wet deposition flux to total for Ni, Cu, Cd and Pb are 17.21%, 40.33%, 7.67% and 0.48%, respectively; the wet deposition flux is far less than dry deposition, especially for Pb. The rate of dry deposition is lower than wet deposition, however dry deposition plays an important role in scavenging heavy metals in arid region. Arid region has a low intensity and frequency of rain event, heavy metals were mainly scavenging by dry deposition attribute to its continuous and dependable process. Dry deposition has much more environmental effect on heavy metal in arid region.

A novel method that utilizes a combination of statistical and clustering techniques is presented in order to classify statistically independent heavy rainstorm events and create a limited number of temporal distribution curves. These curves represent the centers of many unitless cumulative rainstorm events and express the temporal distribution patterns in a probabilistic way. The whole process includes the necessary steps from importing raw precipitation time series data to producing the initially unknown optimal number of representative curves. These hyetographs can be used for stochastic simulation, water resources planning, water quality assessment and global change studying. The present type of analysis is fully unsupervised, as no empirical knowledge of local rainfalls is implicated or any arbitrary introduction of quartiles for grouping as is the case in the pertinent literature. An example using data from a Greek Water Division illustrates that the proposed method produces clusters with superior internal structure and temporal distribution curves that are not coming from the same distribution, in contrast to the results using Huff’s curves classification.