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AirGIS, a Geographic Information Systems (GIS) based air pollution and human exposure modelling system, is routinely used in conjunction with the Operational Street Pollution Model (OSPM®), across the globe, to assess local- or street-scale air pollution. We developed a substantially revised version of AirGIS (hereafter, new AirGIS) as a new modelling system in open-source GIS i.e. PostgreSQL software with its spatial extension PostGIS to (1) optimize the model performance enabling model calculations for a large number of sites over a large geographical area, with limited computing resources (2) replace the outdated programming language Avenue (3) become independent of commercial GIS software. This paper, therefore, aims to describe the overall structure of new AirGIS modelling system together with its strengths and limitations. Furthermore, the new AirGIS has been evaluated against various measured datasets of ambient air pollution (NOx, NO2, PM10 and PM2.5). In terms of reproducing temporal variation (single location, time series of concentrations e.g. annual, daily etc.) of air pollution, the new model achieved correlations (R) in the range 0.45–0.96. While, in terms of reproducing the spatial variation (several locations, single time interval), the new AirGIS achieved correlations in the range 0.32–0.92. The new model, therefore, can be used for both short- and long-term air pollution exposure assessments to facilitate health related studies. However, the present evaluation of the new modelling system also revealed that the new AirGIS significantly overestimated the observed concentrations for two out of four datasets. The possible reasons for these errors and future directions to reducing the bias in the new model output have been discussed.

Deforestation is the removal, clearing or cutting of trees, stand or forest where the land is thereafter converted to the non-forest use. The lands conversions in study area are mostly to farm, agriculture or urban use. The study area was Sangar valley of District Swat. The region is mostly temperate region and enriched by species like Pinus willichina which needs proper documentation and information. The study area is mostly effected by manmade activities like deforestation of precious tree species therefore questionnaire based survey was conducted which contain all the information about the area, trees, cutting, fuel wood consumption, forest degradation and its effects and random questions were asked from local inhabitants. Forest field survey, focus group discussion and semi-structured interviews with 100 respondents was conducted for data collection. The study helped to find out the hidden mafia behind the illegal offence and lumbering activity. Stopping deforestation in the study area can control the loss of biodiversity, provide home to thousands of people and provide irreplaceable ecosystem like cultural and economic functions. Data was analyzed by percentage, frequency and simple fraction formula and Chi square test is applied on data for statistical analysis. Landsat 5 TM scenes and Landsat-8 processing were compared and Normalized difference vegetation index was examined through Arc GIS. The whole degree of the Sangar Valley were acquired from the United States Geological Survey (USGS) Earth Explorer database (USGS, 2013a). All scenes were geo-referenced (UTM, WGS84) and were remedied to USGS “Standard Terrain Correction (Level 1T)” (USGS, 2013b)., Omar Jibran Khan, Bilal Muhammad, Tahir Ali, Saifullah, Murtaza Ali. Underlying factors of deforestation and its effects in Sanger Valley District Swat. J. Bio. Env. Sci. 15(5), 14-28, November 2019. https://innspub.net/jbes/underlying-factors-deforestation-effects-sanger-valley-district-swat/

Road traffic noise is the most pervasive source of ambient outdoor noise pollution in Europe. Traffic noise prediction models vary in parameterisation and therefore may produce different estimates of noise levels depending on the geographical setting in terms of emissions sources and propagation field. This paper compares three such models: the European standard, Common Noise Assessment Methods for the EU Member States (hereafter, CNOSSOS), Nord2000 and Traffic Noise Exposure (TRANEX) model based on the UK methodology, in terms of their source and propagation characteristics. The tools are also compared by analysing estimated noise (LAeq) from CNOSSOS, Nord2000 (2006 version), and TRANEX for more than one hundred test cases (N = 111) covering a variety of source and receiver configurations (e.g. varying source to receiver distance). The main aim of this approach was to investigate the potential pattern in differences between models’ performance for certain types of configurations. Discrepancies in performance may thus be linked to the differences in parameterisations of the CNOSSOS, Nord2000, and TRANEX (e.g. handling of diffraction, refraction). In most cases, both CNOSSOS and TRANEX reproduced LAeq levels of Nord2000 (2006 version) within three to five dBA (CNOSSOS: 87%, TRANEX: 94%). The differences in LAeq levels of CNOSSOS, compared to Nord2000, can be related to several shortcomings of the existing CNOSSOS algorithms (e.g. ground attenuation, multiple diffractions, and mean ground plane). The analyses show that more research is required in order to improve CNOSSOS for its implementation in the EU. In this context, amendments for CNOSSOS proposed by an EU Working Group hold significant potential. Overall, both CNOSSOS and TRANEX produced similar results, with TRANEX reproducing Nord2000 LAeq values slightly better than the CNOSSOS. The lack of measured noise data highlights one of the significant limitations of this study and needs to be addressed in future work.

Air pollution and noise originating from urban road traffic have been linked to the adverse health effects e.g. cardiovascular disease (CVD), although their generation and propagation mechanisms vary. We aimed to (i) develop a tool to model exposures to air pollution and noise using harmonized inputs based on similar geographical structure (ii) explore the relationship (using Spearman's rank correlation) of both pollutions at residential exposure level (iii) investigate the influence of traffic speed and Annual Average Daily Traffic (AADT) on air-noise relationship. The annual average (2005) air pollution (NOx, NO2, PM10, PM2.5) and noise levels (Lday, Leve, Lnight, Lden, LAeq,24h) are modelled at address locations in Copenhagen and Roskilde (N = 11,000 and 1500). The new AirGIS system together with the Operational Street Pollution Model (OSPM®) is used to produce air pollution estimates. Whereas, noise is estimated using Common Noise Assessment Methods in the EU (CNOSSOS-EU, hereafter CNOSSOS) with relatively coarser inputs (100 m CORINE land cover, simplified vehicle composition). In addition, noise estimates (Lday, Leve, Lnight) from CNOSSOS are also compared with noise estimates from Road Traffic Noise 1996 (RTN-96, one of the Nordic noise prediction standards). The overall air-noise correlation structure varied significantly in the range |rS| = 0.01–0.42, which was mainly affected by the background concentrations of air pollution as well as non-traffic emission sources. Moreover, neither AADT nor traffic speed showed substantial influence on the air-noise relationship. The noise levels estimated by CNOSSOS were substantially lower, and showed much lower variation than levels obtained by RTN-96. CNOSSOS, therefore, needs to be further evaluated using more detailed inputs (e.g. 10 m land cover polygons) to assess its feasibility for epidemiological noise exposure studies in Denmark. Lower to moderate air-noise correlations point towards significant potential to determine the independent health effects of air pollution and noise.

Studies on health effects of long-term exposure to specific PM2.5 constituents are few. Previous studies have reported an association between black carbon (BC) exposure and cardiovascular diseases (CVD) and a few studies have found an association between sulfate exposure and mortality. These studies, however, relied mainly on exposure data from centrally located air-monitoring stations, which is a crude approximation of personal exposure. We focused on specific chemical constituents of PM2.5, i.e. elemental and primary organic carbonaceous particles (BC/OC), sea salt, secondary inorganic aerosols (SIA, i.e. NO3 –, NH4 +, and SO4 2-), and secondary organic aerosols (SOA), in relation to all-cause, CVD and respiratory disease mortality. We followed a Danish cohort of 49,564 individuals from enrollment in 1993–1997 through 2015. We combined residential address history from 1979 onwards with mean annual air pollution concentrations obtained by the AirGIS air pollution modelling system, lifestyle information from baseline questionnaires and socio-demography obtained by register linkage. During 895,897 person-years of follow-up, 10,193 deaths from all causes occurred – of which 2319 were CVD-related and 870 were related to respiratory disease. The 15-year time-weighted average concentrations of PM2.5, BC/OC, sea salt, SIA and SOA were 13.8, 2.8, 3.4, 4.9, and 0.3 µg/m3, respectively. For all-cause mortality, a higher risk was observed with higher exposure to PM2.5, BC/OC and SOA with adjusted hazard ratios of 1.03 (95% confidence intervals: 1.01, 1.05), 1.06 (1.03, 1.09), and 1.08 (1.03, 1.13) per interquartile range, respectively. The associations for BC/OC and SOA remained after adjustment for PM2.5 in two-pollutant models. For CVD mortality, we observed elevated risks with higher exposure to PM2.5, BC/OC and SIA. The results showed no clear relationship between sea salt and mortality. In this study, we observed a relationship between long-term exposure to PM2.5, BC/OC, and SOA and all-cause mortality and between PM2.5, BC/OC, and SIA and CVD mortality.

Air pollutants such as NO2 and PM2.5 have consistently been linked to mortality, but only few previous studies have addressed associations with long-term exposure to black carbon (BC) and ozone (O3). We investigated the association between PM2.5, PM10, BC, NO2, and O3 and mortality in a Danish cohort of 49,564 individuals who were followed up from enrollment in 1993-1997 through 2015. Residential address history from 1979 onwards was combined with air pollution exposure obtained by the state-of-the-art, validated, THOR/AirGIS air pollution modelling system, and information on residential traffic noise exposure, lifestyle and socio-demography. We observed higher risks of all-cause as well as cardiovascular disease (CVD) mortality with higher long-term exposure to PM2.5, PM10, BC, and NO2. For PM2.5 and CVD mortality, a hazard ratio (HR) of 1.29 (95% CI: 1.13-1.47) per 5 g/m3 was observed, and correspondingly HRs of 1.16 (95% CI: 1.05-1.27) and 1.11 (95% CI: 1.04-1.17) were observed for BC (per 1 g/m3) and NO2 (per 10 g/m3), respectively. Adjustment for noise gave slightly lower estimates for the air pollutants and CVD mortality. Inverse relationships were observed for O3. None of the investigated air pollutants were related to risk of respiratory mortality. Stratified analyses suggested that the elevated risks of CVD and all-cause mortality in relation to long-term PM, NO2 and BC exposure were restricted to males. This study supports a role of PM, BC, and NO2 in all-cause and CVD mortality independent of road traffic noise exposure. The research was funded by NordForsk under the Nordic Programme on Health and Welfare (Project #75007: NordicWelfAir - Understanding the link between Air pollution and Distribution of related Health Impacts and Welfare in the Nordic countries). The funding source had no involvement in the study design, collection, analysis, interpretation, writing and decision to submit for publication. Scopus

With an ever increasing population and significant solar resource availability throughout the whole year, Karachi metropolis hold a promising rooftop solar photovoltaic (PV) potential considering its millions of urban households. This research work highlights techniques to combine geographic information systems (GIS) and object-based image recognition approach to identify the available rooftop area for PV deployment in a small scale region of DHA Phase 7 Karachi. A six step methodology has been adopted for the estimation of rooftop PV potential which involves geographic division of high resolution satellite imagery; sampling and rooftop feature extraction using FE tool of ENVI EX software; extrapolation of rooftop areas for the entire ROI using roof area-population relationship; visual inspection to analyze different rooftop factors such as building orientation, shading effect from trees and nearby buildings and other roof uses; a comparison of extracted rooftops to the physically measured sample rooftops, reduction for shading and other uses; and finally conversion to energy and power outputs. A relationship of total roof area and population of 13 m2/capita ±5% has been found. With higher efficiency rooftop PV panels, 12.24 MW of solar power can be generated which is 122.4% of peak power demand of DHA Phase 7.

Most of the energy generated globally utilize fossil fuels involving the emission of environmentally hazardous carbon dioxide and depletion of fossil fuel resources. The continuous variation in fuel prices has added a major concern on its sustainable use for future energy requirements. In order to minimize the environmental degradation during energy production process due to emissions of hazardous gases, the utilization of renewable energy resources can make the energy use clean as well as sustainable. Due to an ever increasing demand of clean energy, a sharp rise in the utilization of naturally available solar energy has been observed. Currently, there are several possible routes for solar energy technological developments. In order to effectively utilize the solar power system, one needs to know the technology and its suitability according to the requirements and nature of usage. In this article, different solar power technologies have been reviewed which can be utilized for the global sustainable electric power generation. Major emphasize has been on solar photovoltaic (PV) and concentrated solar power (CSP) technologies. Their types, mechanism, efficiency and cost factors have been discussed. It has been observed that solar PV being more commercially developed and mature technology is suited for both small and large scale applications while CSP technology despite being expensive yield higher economic returns and is suited for large scale applications. Every solar power technology has its own advantage and disadvantage and their preferred usage is basically dependent on the specific case and given conditions. It has also been observed that solar energy, which is a fairly stable and consistently available source of clean energy has the significant potential to cater ever increasing world electricity requirements.

The mega city of Karachi is still mainly dependent on conventional sources of energy to cater its daily electricity requirements. Dependence on conventional sources of energy for power production results in environmental degradation and depletion of fossil fuel resources. In particular, it also highlights an immense need of alternate sustainable solution for current electricity generation scenario. In this research work, an innovative methodology has been proposed to identify bright rooftops using open source geographic information system (GIS) tools which may be utilized for sustainable power generation in Karachi metropolis. First, bright rooftops have been extracted using open source Quantum GIS (QGIS) software. Edge extraction technique using gradient filter; an open source algorithm of QGIS has been utilized. Furthermore, image processing techniques have been used to extract and refine building rooftops. Then, rooftops have been polygonized and their area has been calculated using Measure Area function of QGIS. To assess the accuracy of the extracted rooftops, field validation work has been performed and sample rooftops have been physically measured. A comparison of extracted and physically measured sample rooftops yielded 90.45% accuracy. Reduction in total roof area has been made considering different roof uses and shading effect from nearby trees and buildings. Then, unshaded bright rooftops area of 4,626 m2 has been calculated which can be used for solar photovoltaic (PV) applications in Creek Lanes, DHA Phase 7 Karachi. An annual energy output of 2.1 MWh has been estimated using Crystalline Silicon (c-Si) solar PV panel and available rooftop area. The methodology adopted can be extrapolated to macro-scale as well. However, challenges and limitations for extrapolation of methodology have also been highlighted. Solar radiation studies that demonstrate the use of open source GIS tools for sustainable power generation for this region have been scarce. Thus, this study is a preliminary research work to highlight an immense solar electricity potential that exists for Karachi metropolis.

Road traffic induces air and noise pollution in urban environments having negative impacts on human health. Thus, estimating exposure to road traffic air and noise pollution (hereafter, air and noise pollution) is important in order to improve the understanding of human health outcomes in epidemiological studies. The aims of this review are (i) to summarize current practices of modelling and exposure assessment techniques for road traffic air and noise pollution (ii) to highlight the potential of existing tools and techniques for their combined exposure assessment for air and noise together with associated challenges, research gaps and priorities. The study reviews literature about air and noise pollution from urban road traffic, including other relevant characteristics such as the employed dispersion models, Geographic Information System (GIS)-based tool, spatial scale of exposure assessment, study location, sample size, type of traffic data and building geometry information. Deterministic modelling is the most frequently used assessment technique for both air and noise pollution of short-term and long-term exposure. We observed a larger variety among air pollution models as compared to the applied noise models. Correlations between air and noise pollution vary significantly (0.05–0.74) and are affected by several parameters such as traffic attributes, building attributes and meteorology etc. Buildings act as screens for the dispersion of pollution, but the reduction effect is much larger for noise than for air pollution. While, meteorology has a greater influence on air pollution levels as compared to noise, although also important for noise pollution. There is a significant potential for developing a standard tool to assess combined exposure of traffic related air and noise pollution to facilitate health related studies. GIS, due to its geographic nature, is well established and has a significant capability to simultaneously address both exposures.

The new iterative method has been used to obtain the approximate solutions of time fractional damped Burger and time fractional Sharma-Tasso-Olver equations. Results obtained by the proposed method for different fractional-order derivatives are compared with those obtained by the fractional reduced differential transform method (FRDTM). The 2nd-order approximate solutions by the new iterative method are in good agreement with the exact solution as compared to the 5th-order solution by the FRDTM.

Background:Adverse health effects of air pollution have been reported in previous studies with varying methodological approaches to the exposure assessment. Measuring individual air pollution exposure for large-scale epidemiological studies is infeasible, calling for refined modeling tools. We evalu

Candida glabrata is a human commensal and an opportunistic human fungal pathogen. It is more closely related to the model yeast Saccharomyces cerevisiae than other Candida spp. Compared with S. cerevisiae, C. glabrata exhibits higher innate tolerance to various environmental stressors, including hyperthermal stress. Here we investigate the molecular mechanisms of C. glabrata adaptation to heat stress via adaptive laboratory evolution. We show that all parallel evolved populations readily adapt to hyperthermal challenge (from 47 °C to 50 °C) and exhibit convergence in evolved phenotypes with extensive cross-tolerance to various other environmental stressors such as oxidants, acids, and alcohols. Genome resequencing identified fixation of mutations in CgSTE11 in all parallel evolved populations. The CgSTE11 homolog in S. cerevisiae plays crucial roles in various mitogen-activated protein kinase (MAPK) signaling pathways, but its role is less understood in C. glabrata. Subsequent verification confirmed that CgSTE11 is important in hyperthermal tolerance and the observed extensive cross-tolerance to other environmental stressors. These results support the hypothesis that CgSTE11 mediates cross-talks between MAPK signaling pathways in C. glabrata in response to environmental challenges.

Angle Jungle is an iPad game designed as an educational supplement for fourth to sixth grade students learning about angles. The game asks players to solve puzzles using angles, and incorporates research in numeric-spatial learning, scaffolding tools, and puzzle design to reinforce the intended experience. We describe the game's development from concept to release. Angle Jungle is available as a standalone game on the Apple App Store allowing for both single player and collaborative play inside and outside the classroom.

The performance of the human hand gesture recognition systems depends on the quality of the images presented to the system. Since these systems work in real time environment the images may be corrupted by some environmental noise. By removing the noise the performance of the system can be enhanced. So far different noise removal methods have been presented in many researches to eliminate the noise but all have its own limitations. We have presented a region finding method to deal with the environmental noise that gives better results and enhances the performance of the human hand gesture recognition systems so that the recognition rate of the system can be improved.

Objective Recent studies on air pollution and disease have been based on millions of participants within a region or country, relying entirely on register-based confounder adjustment. We aimed to investigate the effects of increasing adjustment for register- and questionnaire-based covariates on the association between air pollution and cardiometabolic diseases. Methods In a population-based cohort of 246,766 eligible participants randomly selected across Denmark in 2010 and 2013 and followed up until December 31, 2017, we identified 3,247 myocardial infarction (MI) cases, 4,166 stroke cases and 6,366 type 2 diabetes cases. Based on historical address-information, we calculated 5-year time-weighted exposure to PM2.5 and NO2 modelled using a validated air pollution model. We used Cox proportional hazards models to calculate hazard ratios (HR) with increasing adjustment for a number of individual- and area-level register-based covariates as well as lifestyle covariates assessed through questionnaires. Results We found that a 5 μg/m3 higher PM2.5 was associated with HRs (95% CI) for MI, stroke and diabetes, of respectively, 1.18 (0.91–1.52), 1.11 (0.88–1.40) and 1.24 (1.03–1.50) in the fully adjusted models. For all three diseases, adjustment for either individual-level, area-level or lifestyle covariates, or combinations of these resulted in higher HRs compared to HRs adjusted only for age, sex and calendar-year, most marked for MI and diabetes. Further adjustment for lifestyle in models with full register-based individual- and area-level adjustment resulted in only minor changes in HRs for all three diseases. Conclusions Our findings suggest that in studies of air pollution and cardiometabolic disease, which use an adjustment strategy with a broad range of register-based socioeconomic variables, there is no effect on risk estimates from subsequent lifestyle adjustment.

Modelling of ambient particle number concentrations (PNC) has been implemented in the Danish air quality modelling system DEHM/UBM/AirGIS and evaluated with long-term measurements. We implemented particle dynamical processes in the regional scale model DEHM using the M7 aerosol dynamics module (presented in the accompanying article by Frohn et al., 2021), and we developed models for PNC at the local scale (UBM) and street scale (OSPM), in a first approximation without including the particle dynamics as presented in this article. Outdoor concentration estimates are provided at the front door of all residential address locations in Denmark for the past 40 years (1979–2018) with a spatial resolution of 1 km × 1 km taking all emission sectors in Denmark into account and additionally at the street location, with significant traffic (>500 vehicles/day). We evaluated our model with up to 18-year long measurement time series of particle number size distributions (PNSD) at Danish street, urban and rural background stations. Two particle size ranges were used for evaluation: PNC >10 (count of particles with diameter larger than 10 nm) and PNC 30_250 (diameter range 30–250 nm), in order to exclude nucleation events from the measurements and to obtain a consistent long-term measured time series. When comparing our model estimates with PNC 30_250 measurements, we obtain Pearson correlation coefficients (R p) in the range 0.39–0.95 depending on station location (street, urban background, rural) and averaging time (hour, day, month, year). The highest correlations were found for yearly averages at a monitoring station located at a street with dense traffic (R p = 0.95) whereas shorter time averages and comparisons with monitoring stations at urban and rural background locations provided lower correlations. The model performance for PNC in terms of correlation coefficients with respect to measurements is comparable to the performance for other pollutants such as NO X, PM 2.5 and better than the performance for PM 10. The model generally overestimated the observed concentrations, Normalized Mean Bias (NMB) was in the range 6%–190% compared to PNC >10 and 90%–290% compared to PNC 30_250. These relatively high NMBs are probably caused by uncertainties in the modelling process, especially the estimation of particle number emissions, which largely determine the ambient concentrations of PNC. Furthermore, uncertainties might as well originate from the complexity of modelling particle dynamical processes accurately and the great challenges in performing long-term PNC measurements. The presented model can estimate PNC at all Danish addresses over the last 40 years with a 1-h time resolution. The data seem to provide a good indication of the relative differences in PNC at Danish addresses.