The synergistic effect of air mass on outdoor performance for different PV module in India

The effect of air mass (AM) on the performance of multi-crystalline silicon (m-Si), amorphous silicon (a-Si), and hetero-junction with intrinsic thin layer (HIT)-technology-based photovoltaic (PV) modules are evaluated for representative day of four seasons during the year 2011 for composite climate of India. To find the best performing PV module technology with respect to AM at the site, annual energy yields and performance ratio against different AM bands (AM 1–4.5) are plotted. It is found that HIT modules perform better than m-Si and a-Si at each AM band. Annual energy yields for all three technologies decrease with increasing order of AM bands. The performance ratio for HIT and m-SI modules initially increases and then decreases with increasing order of AM bands. However, for a-Si modules, the performance ratio decreases with increasing order of the AM bands.     

Oxidative properties of ambient PM2.5 and elemental composition: Heterogeneous associations in 19 European cities

We assessed the extent to which constituents of PM_2.5 (transition metals, sodium, chloride) contribute to the ability to generate hydroxyl radicals (OH) in vitro in PM_2.5 sampled at 20 locations in 19 European centres participating in the European Community Respiratory Health Survey. PM_2.5 samples (n = 716) were collected on filters over one year and the oxidative activity of particle suspensions obtained from these filters was then assessed by measuring their ability to generate OH in the presence of hydrogen peroxide. Associations between OH formation and the studied PM constituents were heterogeneous. The total explained variance ranged from 85% in Norwich to only 6% in Albacete. Among the 20 centres, 15 showed positive correlations between one or more of the measured transition metals (copper, iron, manganese, lead, vanadium and titanium) and OH formation. In 9 of 20 centres OH formation was negatively associated with chloride, and in 3 centres with sodium. Across 19 European cities, elements which explained the largest variations in OH formation were chloride, iron and sodium.     

Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations

The new method for the forecasting hourly concentrations of air pollutants is presented in the paper. The method was developed for a site in urban residential area in city of Zagreb, Croatia, for four air pollutants (NO₂, O₃, CO and PM₁₀). Meteorological variables and concentrations of the respective pollutant were taken as predictors. A novel approach, based on families of univariate regression models, was employed in selecting the averaging intervals for input variables. For each variable and each averaging period between 1 and 97 h, a separate model was built. By inspecting values of the coefficient of correlation between measured and modelled concentrations, optimal averaging periods for each variable were selected. A new dataset for building the forecasting model was then calculated as temporal moving averages (running means) of former variables. A multi-layer perceptron type of neural networks is used as the forecasting model. Index of agreement, calculated for the entire dataset including the data for model building, ranged from 0.91 to 0.97 for the respective pollutants. As suggested by the analysis of the relative importance of the input variables, different agreements for different pollutants are likely due to different sources and production mechanisms of investigated pollutants. A comparison of the new method with more traditional method, which takes hourly averages of the forecast hour as input variables, showed similar or better performance. The model was developed for the purpose of public-health-oriented air quality forecasting, aiming to use a numerical weather forecast model for the prediction of the part of input data yet unknown at the forecasting time. It is to expect that longer term averages used as inputs in the proposed method will contribute to smaller input errors and the greater accuracy of the model.     

White clover clones as a cost-effective indicator of phytotoxic ozone: 10 years of experience from central Italy

Data collected at one site in central Italy using the NC-S/NC-R clover (Trifolium repens) biotype system during 1997-2007 were analysed in order to assess: (a) its performance under Mediterranean conditions; (b) variations of ozone damage linked with meteorological conditions; (c) if critical level approach is a good predictor of ozone risk on vegetation. NC-S dry biomasses were systematically lower than those of NC-R, the mean ratio being 0.7. Relevant relationship between ozone visible injury and cumulated values of AOT40 were also reported. Temperature and number of rainy days were the most important factors associated with ozone presence and, as a consequence, with leaf injury index. Photosynthetic gas exchange properties indicate that NC-S has higher values of stomatal conductance.     

Assessment of present and future risk to Italian forests and human health: Modelling and mapping

A review of ozone pollution in Italy shows levels largely above the thresholds established by EU regulation for vegetation and human health protection. The Italian air quality monitoring network appears quantitatively inadequate to cover all the territorial surface, because of scarcity and unequal distribution of monitoring sites. By applying the integrated assessment model RAINS-Italy to the year 2000, the whole of Italy exceeds the AOT40 critical level for forest, while Northern and central areas show strong potential of O₃ impact on human health with ∼11% of territory >10 O₃-induced premature deaths. Two scenarios for the year 2020, the Current Legislation and the Maximum Technical Feasible Reduction, show a reduction of AOT40Forest by 29% and 44%, SOMO35 by 31% and 47%, and O₃-induced premature deaths by 32% and 48%, compared to 2000. RAINS-Italy can be used to improve the map quality and cover areas not reached by the national monitoring network.     

Determination of direct and fugitive PM emissions in a Mediterranean harbour by means of classic and novel tracer methods

Remodelling works are frequent in harbour areas, given that they must adapt to rapidly changing market requirements. Depending on their nature (construction, demolition, etc.), these works may be carried out during long periods of time and thus exert a significant impact on the air quality at the harbours and their surroundings. The air quality at the Valencia harbour was studied during an enlargement project. Four sampling stations aimed to cover the major dust-emitting activities in the harbour. In addition, a sampling campaign was carried out to collect dust materials at their emission sources (e.g., loading and unloading of clinker and petroleum coke, as well as the enlargement works). Differences obtained between PM levels at the monitoring sites were correlated with the distance between sampling stations and enlargement works and/or harbour operations, as well as with the preferential wind direction. In all cases, the days with the highest PM_10 levels were linked to wind directions coinciding with the location of the enlargement works or the clinker and petroleum coke stocks. Classic source apportionment methodologies (PCA and CMB) were applied to the data, but their interpretation was complex due to the similar chemical signatures of PM originating from direct and fugitive emissions from stocked materials (e.g., clinker), and the enlargement works. To overcome this difficulty, a novel non-statistical approach was used to obtain quantitative estimations of the contributions from sources (petroleum coke and clinker), based on the correlation between specific PM components (e.g., carbonaceous species) and source tracers (e.g., V). Finally, a qualitative test using phenolphthalein was devised to identify the presence of clinker on the filters, which provided positive results. This novel tracer approach is thus recommended for harbour authorities.     

Artificial neural network based carbon monoxide persistence models for episodic urban air quality management

This paper describes the development of artificial neural network (ANN) based carbon monoxide (CO) persistence (ANNCOP) models to forecast 8-h average CO concentration using 1-h maximum predicted CO data for the critical (winter) period (November–March). The models have been developed for three 8-h groupings of 10 p.m. to 6 a.m., 6 a.m. to 2 p.m. and 2–10 p.m., at two air quality control regions (AQCRs) in Delhi city, representing an urban intersection and an arterial road consisting heterogeneous traffic flows. The result indicates that time grouping of 2–10 pm is dominantly affected by inversion conditions and peak traffic flow. The ANNCOP model corresponding to this grouping predicts the 8-h average CO concentrations within the accuracy range of 68–71%. The CO persistence values derived from ANNCOP model are comparable with the persistence values as suggested by the Environmental Protection Agency (EPA), USA. This work demonstrates that ANN based model is capable of describing winter period CO persistence phenomena.     

Carbon Monoxide Concentration in a Street Canyon of Buenos Aires City (Argentina)

The analysis of three years of 8-h CO concentration values registered in a deep street canyon downtown shows high frequency of values that exceed WHO health protection guidelines. An inverse relationship between opposing percentiles of the distributions of CO concentrations and mean wind speed could be found. Data also showed a variation of mean CO values with prevailing wind direction. The averaged concentration value obtained when the sampler probe is on the leeward side is lower than the obtained when it is on the windward wall. A preliminary explanation of this feature may be related to the advection of polluted air from a high traffic density area nearby.     

Biomonitoring of heavy metals by pollen in urban environment

Abstract Industrial development and consumption of petroleum products leads to increase air pollution levels especially in urban and industrial areas. Heavy metal components associated with air pollutants have far reaching effects with respect to economic and ecological importance of pollens. The pollens are male reproductive organs of the plant and travel through air from flower to flower for pollination purpose. During this period they are exposed to air pollutants. Present investigation thus pertains to study of effect of air pollutants on pollens especially biosorption and bioaccumulation of heavy metals. The pollens of three commonly occurring plants namely Cassia siamea, Cyperus rotundus, Kigelia pinnata have been studied from the NH-6 of Nagpur city, India. The pollens exposed to polluted air showed the presence of higher concentrations of Ca, Al and Fe as compared to unexposed pollens. Higher concentration of these metals was observed in Cyperus rotundus followed by Cassia siamea and Kigelia pinnata. These results indicate that pollens act as good indicator of air pollution giving results in short time of exposure of 5–10 h. Apart from this, it is also reported that some of these metals play crucial role in the metabolic activity in pollens for example Calcium is necessary for growth of pollen tube and other metabolic activities in pollens. The presence of these metals in pollens may also enhance the allergenicity of the pollens. Similarly accumulation of heavy metals may also deteriorate the quality of pollen for their economical use. The viability of pollen is also affected by these pollutants in sensitive species leading to impairment of their fertility.     

Using the polychaete Arenicola marina to determine toxicity and bioaccumulation of PAHS bound to sediments

Transportation system has contributed significantly to the development of human civilization; on the other hand it has an enormous impact on the ambient air quality in several ways. In this paper the air and noise pollution at selected sites along three sections of National Highway was monitored. Pakistan National Highway Authority has started a Highway Improvement program for rehabilitations and maintenance of National highways to improve the traffic flows, and would ultimately improve the air quality along highways. The ambient air quality and noise level was monitored at nine different locations along these sections of highways to quantify the air pollution. The duration of monitoring at individual location was 72 h. The most of the sampling points were near the urban or village population, schools or hospitals, in order to quantify the air pollution at most affected locations along these roads. A database consisting of information regarding the source of emission, local metrology and air quality may be created to assess the profile of air quality in the area.