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.     

Calculating losses of crops in Denmark caused by high ozone levels

In order to help guide air pollution legislation at the European level, harmful air pollution effects on agriculture crops and the consequent economic implications for policy have been studied for more than a decade. Ozone has been labeled as the most serious of the damaging air pollutants to agriculture, where growth rates and consequently yields are dramatically reduced. Quantifying the effects has formed a key factor in policymaking. Based on the widely held view that AOT40 (Accumulated exposure Over Threshold of 40 ppb) is a good indicator of ozone-induced damage, the Danish Eulerian Model (DEM) was used to compute reduced agriculture yields on a 50 km×50 km grid over Europe. In one set of scenarios, a ten year meteorological time series was combined with realistic emission inventories. In another, various idealized emission reduction scenarios are applied to the same meteorological time series. The results show substantial inter-annual variability in economic losses, due in most part to meteorological conditions which varied much more substantially than the emissions during the same period. It is further shown that, taking all uncertainties into account, estimates of ozone-induced economic losses require that a long meteorological record is included in the analysis, for statistical significance to be improved to acceptable levels for use in policy analysis. In this study, calculations were made for Europe as a whole, though this paper presents results relevant for Denmark.     

Migratory crustaceans as biomonitors of metal pollution in their nursery areas. The Lesina lagoon (SE Italy) as a case study

Lichens collected in an unpolluted forest (background) in November 2004, transplanted at 41 sampling sites along the Madina-Tetteh Quarshie road, retrieved in February 2005 and analysed were found to contain higher concentrations of Manganese (Mn), Vanadium (V), lead, Cadmium (Cd), Chromium (Cr) and Nickel (Ni) than in the background area. Observations showed that of all the heavy metals studied, Manganese concentrations were highest at all sampling points with maximum values around a traffic light, an intersection and car fitting/mechanic workshops. Significant correlations were found between Ni and Mn, Mn and Cr and Mn and V. Statistical analysis also revealed a relatively even dispersion of the studied elements on the eastern side of the road than on the western side.     

Application of GIS in the study of mass transport of pollutants by Adyar and Cooum Rivers in Chennai, Tamilnadu

Residential, industrial, commercial, institutional and recreational activities discharge degradable and non-degradable wastes that reach the coastal water through rivers and cause coastal pollution. In the present study, mass transport of pollutants by Adyar and Cooum Rivers to the coastal water as a result of land-based discharges was estimated during low tide. The lowest and the highest flow recorded in Adyar varied from 514.59 to 2,585.08×10⁶ litres/day. Similarly, the flow in Cooum River fluctuated between 266.45 and 709.34×10⁶ litres/day. The present study revealed that the Adyar River transported 53.89–454.11 t/d of suspended solids, 0.06–19.64 t/d of ammonia, 15.95–123.24 t/d of nitrate and 0.4–17.86 t/d of phosphate, 0.004–0.09 kg/d of cadmium, 0.15–1.29 kg/d of lead and 3.03–17.58 kg/d of zinc to the coastal water owing to its high discharge. Similarly, the Cooum River transported 11.87–120.06 t/d of suspended solids, 0.08–58.7 t/d of ammonia, 6.11–29.25 t/d of nitrate and 0.66–10.73 t/d of phosphate, 0.003–0.021 kg/d of cadmium, 0.02–0.44 kg/d of lead and 1.36–3.87 kg/d of zinc. A higher concentration of suspended solids was noticed in post monsoon and summer months. An increase in the mass transport of ammonia, nitrate, phosphate in summer months (April and May) and an increase in the mass transport of cadmium, lead and zinc were observed in monsoon months (October–December) in both the rivers. Thus mass transport of pollutants study reveal that Cooum and Adyar Rivers in Chennai contribute to coastal pollution by transporting inorganic and trace metals significantly through land drainage.     

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.     

Modelling Tracer Dispersion from Landfills

Several wind tunnel experiments of tracer dispersion from reduced-scale landfill models are presented in this paper. Different experimental set-ups, hot-wire anemometry, particle image velocimetry and tracer concentration measurements were used for the characterisation of flow and dispersion phenomena nearby the models. The main aim of these experiments is to build an extensive experimental data set useful for model validation purposes. To demonstrate the potentiality of the experimental data set, a validation exercise on several mathematical models was performed by means of a statistical technique. The experiments highlighted an increase in pollutant ground level concentrations immediately downwind from the landfill because of induced turbulence and mean flow deflection. This phenomenon turns out to be predominant for the dispersion process. Tests with a different set-up showed an important dependence of the dispersion phenomena from the landfill height and highlighted how complex orographic conditions downwind of the landfill do not affect significantly the dispersion behaviour. Validation exercises were useful for model calibration, improving code reliability, as well as evaluating performances. The Van Ulden model proved to give the most encouraging results.     

Hydrologic–Economic Model for Managing Irrigation Intensity in Irrigation Areas under Watertable and Soil Salinity Targets

This paper gives mathematical details and sample applications of SWAGMAN Farm (SWAGMAN, Salt Water and Groundwater Management), a farm-scale hydrologic economic model that integrates agronomic, climatic, irrigation, hydrogeological and economic aspects of irrigated agriculture. The model is capable of determining optimum mix of land use to keep watertable and soil salinity within acceptable limits while maximising the economic returns. Alternatively, the model can simulate water and salt balance and economics of a given cropping preference. Web-based and Geographic Information Systems versions of the model are available for integration with the environmental reporting systems of the irrigation areas.     

迎难而上 大力推进新疆污染减排工作

在深刻论述新疆地区近年来开展节能减排和总量控制工作情况的基础上,针对我区污染物总量控制工作所面临的形势和问题进行了认真总结,并结合我区今后经济发展要求,为确保完成“十一五”总量控制目标,认真做好“十二五”总量控制工作,提出了加强污染源监督管理、完善考核制度、推进大集团战略及提高环境准入条件等8个方面的对策和建议。     

Statistical Analysis of Air Pollutants and Meteorological Parameters in Afyon, Turkey

In this study, the relation between sulfur dioxide (SO₂) and particulate matter (PM) concentrations periodically measured in the city of Afyon’s atmosphere with meteorological factors such as precipitation, humidity, temperature, wind velocity, and inversion were investigated. The mean values of SO₂ and PM concentrations measured during the winter months of October–March 1990–1999 were correlated with the meteorological parameters of the same period. Simple and multiple linear regression analysis were utilized to evaluate the contribution of meteorological variables. The statistical results show that the pollutants, i.e., SO₂ and PM are dependent upon humidity, temperature, and inversion at the 1% significance level; while the dependence of both pollutants with temperature is negative when those of humidity and inversion are positive. Two models in which temperature and inversion are dependent with multiple variables are recommended for predicting the contribution of meteorological parameters on SO₂ and PM. In addition, the relationship between humidity, temperature, and inversion with pollutants is also determined using nonlinear (polynomial) models.