Artificial Neural Network Prediction of Tropospheric Ozone Concentrations in Istanbul,Turkey

Tropospheric (ground‐level) ozone has adverse effects on human health and environment. In this study, next day's maximum 1‐h average ozone concentrations in Istanbul were predicted using multi‐layer perceptron (MLP) type artificial neural networks (ANNs). Nine meteorological parameters and nine air pollutant concentrations were utilized as inputs. The total 578 datasets were divided into three groups: training, cross‐validation, and testing. When all the 18 inputs were used, the best performance was obtained with a network containing one hidden layer with 24 neurons. The transfer function was hyperbolic tangent. The correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), and index of agreement or Willmott's Index (d₂) for the testing data were 0.90, 8.78 µg/m³, 11.15 µg/m³, and 0.95, respectively. Sensitivity analysis has indicated that the persistence information (current day's maximum and average ozone concentrations), NO concentration, average temperature, PM₁₀, maximum temperature, sunshine time, wind direction, and solar radiation were the most important input parameters. The values of R, MAE, RMSE, and d₂ did not change considerably for the MLP model using only these nine inputs. The performances of the MLP models were compared with those of regression models (i.e., multiple linear regression and multiple non‐linear regression). It has been found that there was no significant difference between the ANN and regression modeling techniques for the forecasting of ozone concentrations in Istanbul.     

A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series

Developing a hydrological forecasting model based on past records is crucial to effective hydropower reservoir management and scheduling. Traditionally, time series analysis and modeling is used for building mathematical models to generate hydrologic records in hydrology and water resources. Artificial intelligence (AI), as a branch of computer science, is capable of analyzing long-series and large-scale hydrological data. In recent years, it is one of front issues to apply AI technology to the hydrological forecasting modeling. In this paper, autoregressive moving-average (ARMA) models, artificial neural networks (ANNs) approaches, adaptive neural-based fuzzy inference system (ANFIS) techniques, genetic programming (GP) models and support vector machine (SVM) method are examined using the long-term observations of monthly river flow discharges. The four quantitative standard statistical performance evaluation measures, the coefficient of correlation (R), Nash–Sutcliffe efficiency coefficient (E), root mean squared error (RMSE), mean absolute percentage error (MAPE), are employed to evaluate the performances of various models developed. Two case study river sites are also provided to illustrate their respective performances. The results indicate that the best performance can be obtained by ANFIS, GP and SVM, in terms of different evaluation criteria during the training and validation phases.     

Predicting spatio-temporal concentrations of PM<Subscript>2.5</Subscript> using land use and meteorological data in Yangtze River Delta,China

The prediction of PM_2.5 concentrations with high spatiotemporal resolution has been suggested as a potential method for data collection to assess the health effects of exposure. This work predicted the weekly average PM_2.5 concentrations in the Yangtze River Delta, China, by using a spatio-temporal model. Integrating land use data, including the areas of cultivated land, construction land, and forest land, and meteorological data, including precipitation, air pressure, relative humidity, temperature, and wind speed, we used the model to estimate the weekly average PM_2.5 concentrations. We validated the estimated effects by using the cross-validated R² and Root mean square error (RMSE); the results showed that the model performed well in capturing the spatiotemporal variability of PM_2.5 concentration, with a reasonably large R² of 0.86 and a small RMSE of 8.15 (μg/m³). In addition, the predicted values covered 94% of the observed data at the 95% confidence interval. This work provided a dataset of PM_2.5 concentration predictions with a spatiotemporal resolution of 3 km × week, which would contribute to accurately assessing the potential health effects of air pollution.     

Seasonal Short‐Term Prediction of Dissolved Oxygen in Rivers via Nature‐Inspired Algorithms

This study challenges the use of three nature‐inspired algorithms as learning frameworks of the adaptive‐neuro‐fuzzy inference system (ANFIS) machine learning model for short‐term modeling of dissolved oxygen (DO) concentrations. Particle swarm optimization (PSO), butterfly optimization algorithm (BOA), and biogeography‐based optimization (BBO) are employed for developing predictive ANFIS models using seasonal 15 min data collected from the Rock Creek River in Washington, DC. Four independent variables are used as model inputs including water temperature (T), river discharge (Q), specific conductance (SC), and pH. The Mallow's Cp and R² parameters are used for choosing the best input parameters for the models. The models are assessed by several statistics such as the coefficient of determination (R²), root‐mean‐square error (RMSE), Nash–Sutcliffe efficiency, mean absolute error, and the percent bias. The results indicate that the performance of all‐nature‐inspired algorithms is close to each other. However, based on the calculated RMSE, they enhance the accuracy of standard ANFIS in the spring, summer, fall, and winter around 13.79%, 15.94%, 6.25%, and 12.74%, respectively. Overall, the ANFIS‐PSO and ANFIS‐BOA provide slightly better results than the other ANFIS models.     

Mass fraction spatiotemporal geostatistics and its application to map atmospheric polycyclic aromatic hydrocarbons after 9/11

This work proposes a space/time estimation method for atmospheric PM_2.5 components by modelling the mass fraction at a selection of space/time locations where the component is measured and applying the model to the extensive PM_2.5 monitoring network. The method we developed utilizes the nonlinear Bayesian maximum entropy framework to perform the geostatistical estimation. We implemented this approach using data from nine carcinogenic, particle-bound polycyclic aromatic hydrocarbons (PAHs) measured from archived PM_2.5 samples collected at four locations around the World Trade Center (WTC) from September 22, 2001 to March 27, 2002. The mass fraction model developed at these four sites was used to estimate PAH concentrations at additional PM_2.5 monitors. Even with limited PAH data, a spatial validation showed the application of the mass fraction model reduced the mean squared error (MSE) by 7–22%, while in the temporal validation there was an exponential improvement in MSE positively associated with the number of days of PAH data removed. Our results include space/time maps of atmospheric PAH concentrations in the New York area after 9/11.     

Bayesian autoregressive spatiotemporal model of PM<Subscript>10</Subscript> concentrations across Peninsular Malaysia

Rapid industrialization and haze episodes in Malaysia ensure pollution remains a public health challenge. Atmospheric pollutants such as PM₁₀ are typically variable in space and time. The increased vigilance of policy makers in monitoring pollutant levels has led to vast amounts of spatiotemporal data available for modelling and inference. The aim of this study is to model and predict the spatiotemporal daily PM₁₀ levels across Peninsular Malaysia. A hierarchical autoregressive spatiotemporal model is applied to daily PM₁₀ concentration levels from thirty-four monitoring stations in Peninsular Malaysia during January to December 2011. The model set in a three stage Bayesian hierarchical structure comprises data, process and parameter levels. The posterior estimates suggest moderate spatial correlation with effective range 157 km and a short term persistence of PM₁₀ in atmosphere with temporal correlation parameter 0.78. Spatial predictions and temporal forecasts of the PM₁₀ concentrations follow from the posterior and predictive distributions of the model parameters. Spatial predictions at the hold-out sites and one-step ahead PM₁₀ forecasts are obtained. The predictions and forecasts are validated by computing the RMSE, MAE, R² and MASE. For the spatial predictions and temporal forecasting, our results indicate a reasonable RMSE of 10.71 and 7.56, respectively for the spatiotemporal model compared to RMSE of 15.18 and 12.96, respectively from a simple linear regression model. Furthermore, the coverage probability of the 95% forecast intervals is 92.4% implying reasonable forecast results. We also present prediction maps of the one-step ahead forecasts for selected day at fine spatial scale.     

A simple temperature method for the estimation of evapotranspiration

Accurate estimation of evapotranspiration (ET) is essential in water resources management and hydrological practices. Estimation of ET in areas, where adequate meteorological data are not available, is one of the challenges faced by water resource managers. Hence, a simplified approach, which is less data intensive, is crucial. The FAO‐56 Penman–Monteith (FAO‐56 PM) is a sole global standard method, but it requires numerous weather data for the estimation of reference ET. A new simple temperature method is developed, which uses only maximum temperature data to estimate ET. Ten class I weather stations data were collected from the National Meteorological Agency of Ethiopia. This method was compared with the global standard PM method, the observed Piche evaporimeter data, and the well‐known Hargreaves (HAR) temperature method. The coefficient of determination (R²) of the new method was as high as 0.74, 0.75, and 0.91, when compared with that of PM reference evapotranspiration (ET_o), Piche evaporimeter data, and HAR methods, respectively. The annual average R² over the ten stations when compared with PM, Piche, and HAR methods were 0.65, 0.67, and 0.84, respectively. The Nash–Sutcliff efficiency of the new method compared with that of PM was as high as 0.67. The method was able to estimate daily ET with an average root mean square error and an average absolute mean error of 0.59 and 0.47 mm, respectively, from the PM ET_o method. The method was also tested in dry and wet seasons and found to perform well in both seasons. The average R² of the new method with the HAR method was 0.82 and 0.84 in dry and wet seasons, respectively. During validation, the average R² and Nash–Sutcliff values when compared with Piche evaporation were 0.67 and 0.51, respectively. The method could be used for the estimation of daily ET_o where there are insufficient data. Copyright © 2013 John Wiley & Sons, Ltd.     

Modelling soil moisture in a high‐latitude landscape using LiDAR and soil data

Soil moisture has a pronounced effect on earth surface processes. Global soil moisture is strongly driven by climate, whereas at finer scales, the role of non‐climatic drivers becomes more important. We provide insights into the significance of soil and land surface properties in landscape‐scale soil moisture variation by utilizing high‐resolution light detection and ranging (LiDAR) data and extensive field investigations. The data consist of 1200 study plots located in a high‐latitude landscape of mountain tundra in north‐western Finland. We measured the plots three times during growing season 2016 with a hand‐held time‐domain reflectometry sensor. To model soil moisture and its temporal variation, we used four statistical modelling methods: generalized linear models, generalized additive models, boosted regression trees, and random forests. The model fit of the soil moisture models were R² = 0.60 and root mean square error (RMSE) 8.04 VWC% on average, while the temporal variation models showed a lower fit of R² = 0.25 and RMSE 13.11 CV%. The predictive performances for the former were R² = 0.47 and RMSE 9.34 VWC%, and for the latter R² = 0.01 and RMSE 15.29 CV%. Results were similar across the modelling methods, demonstrating a consistent pattern. Soil moisture and its temporal variation showed strong heterogeneity over short distances; therefore, soil moisture modelling benefits from high‐resolution predictors, such as LiDAR based variables. In the soil moisture models, the strongest predictor was SAGA (System for Automated Geoscientific Analyses) wetness index (SWI), based on a 1 m² digital terrain model derived from LiDAR data, which outperformed soil predictors. Thus, our study supports the use of LiDAR based SWI in explaining fine‐scale soil moisture variation. In the temporal variation models, the strongest predictor was the field‐quantified organic layer depth variable. Our results show that spatial soil moisture predictions can be based on soil and land surface properties, yet the temporal models require further investigation. Copyright © 2017 John Wiley & Sons, Ltd.     

Eddy covariance‐based evapotranspiration for a subtropical wetland

The eddy covariance (EC) method was used in a 30‐month study to quantify evapotranspiration (ET) and vegetation coefficient (K_CW) for a wetland on a ranch in subtropical south Florida. To evaluate the errors in ET estimates, the EC‐based ET (ET_C‐EC) and the Food and Agricultural Organization (FAO) Penman–Monteith (PM) based ET (ET_C‐PM) estimates (with literature crop coefficient, K_C) were compared with each other. The ET_C‐EC and FAO‐PM reference ET were used to develop K_CW. Regression models were developed to estimate K_CW using climatic and hydrologic variables. Annual and daily ET_C‐EC values were 1152 and 3.27 mm, respectively. The FAO‐PM model underestimated ET by 25% with ET_C‐EC being statistically higher than ET_C‐PM. The K_CW varied from 0.79 (December) to 1.06 (November). The mean K_CW for the dry (November–April) season (0.95) was much higher than values reported for wetlands in literature; whereas for the wet (May–October) season, K_CW (0.97) was closer to literature values. Higher than expected K_CW values during the dry season were due to higher temperature, lower humidity and perennial wetland vegetation. Regression analyses showed that factors affecting the K_CW were different during the dry (soil moisture, temperature and relative humidity) and wet (net radiation, inundation and wind speed) seasons. Separate regression models for the dry and wet seasons were developed. Evapotranspiration and K_CW from this study, one of the first for the agricultural wetlands in subtropical environment, will help improve the ET estimates for similar wetlands. Copyright © 2013 John Wiley & Sons, Ltd.     

Developing an empirical model of canopy water flux describing the common response of transpiration to solar radiation and VPD across five contrasting woodlands and forests

A modified Jarvis–Stewart model of canopy transpiration (E_c) was tested over five ecosystems differing in climate, soil type and species composition. The aims of this study were to investigate the model's applicability over multiple ecosystems; to determine whether the number of model parameters could be reduced by assuming that site‐specific responses of E_c to solar radiation, vapour pressure deficit and soil moisture content vary little between sites; and to examine convergence of behaviour of canopy water‐use across multiple sites. This was accomplished by the following: (i) calibrating the model for each site to determine a set of site‐specific (SS) parameters, and (ii) calibrating the model for all sites simultaneously to determine a set of combined sites (CS) parameters. The performance of both models was compared with measured E_c data and a statistical benchmark using an artificial neural network (ANN). Both the CS and SS models performed well, explaining hourly and daily variation in E_c. The SS model produced slightly better model statistics [R² = 0.75–0.91; model efficiency (ME) = 0.53–0.81; root mean square error (RMSE) = 0.0015–0.0280 mm h^‐1] than the CS model (R² = 0.68–0.87; ME = 0.45–0.72; RMSE = 0.0023–0.0164 mm h^‐1). Both were highly comparable with the ANN (R² = 0.77–0.90; ME = 0.58–0.80; RMSE = 0.0007–0.0122 mm h^‐1). These results indicate that the response of canopy water‐use to abiotic drivers displayed significant convergence across sites, but the absolute magnitude of E_c was site specific. Period totals estimated with the modified Jarvis–Stewart model provided close approximations of observed totals, demonstrating the effectiveness of this model as a tool aiding water resource management. Analysis of the measured diel patterns of water use revealed significant nocturnal transpiration (9–18% of total water use by the canopy), but no Jarvis–Stewart formulations are able to capture this because of the dependence of water‐use on solar radiation, which is zero at night. Copyright © 2012 John Wiley & Sons, Ltd.     

ARIMA forecasting of ambient air pollutants (O3, NO, NO2 and CO)

In the present study, a stationary stochastic ARMA/ARIMA [Autoregressive Moving (Integrated) Average] modelling approach has been adapted to forecast daily mean ambient air pollutants (O₃, CO, NO and NO₂) concentration at an urban traffic site (ITO) of Delhi, India. Suitable variance stabilizing transformation has been applied to each time series in order to make them covariance stationary in a consistent way. A combination of different information-criterions, namely, AIC (Akaike Information Criterion), HIC (Hannon–Quinn Information Criterion), BIC (Bayesian Information criterion), and FPE (Final Prediction Error) in addition to ACF (autocorrelation function) and PACF (partial autocorrelation function) inspection, has been tried out to obtain suitable orders of autoregressive (p) and moving average (q) parameters for the ARMA(p,q)/ARIMA(p,d,q) models. Forecasting performance of the selected ARMA(p,q)/ARIMA(p,d,q) models has been evaluated on the basis of MAPE (mean absolute percentage error), MAE (mean absolute error) and RMSE (root mean square error) indicators. For 20 out of sample forecasts, one step (i.e., one day) ahead MAPE for CO, NO₂, NO and O₃, have been found to be 13.6, 12.1, 21.8 and 24.1%, respectively. Given the stochastic nature of air pollutants data and in the light of earlier reported studies regarding air pollutants forecasts, the forecasting performance of the present approach is satisfactory and the suggested forecasting procedure can be effectively utilized for short term air quality forewarning purposes.     

Spatial interpolation of snow water equivalency using surface observations and remotely sensed images of snow‐covered area

As demand for water continues to escalate in the western Unites States, so does the need for accurate monitoring of the snowpack in mountainous areas. In this study, we describe a simple methodology for generating gridded‐estimates of snow water equivalency (SWE) using both surface observations of SWE and remotely sensed estimates of snow‐covered area (SCA). Multiple regression was used to quantify the relationship between physiographic variables (elevation, slope, aspect, clear‐sky solar radiation, etc.) and SWE as measured at a number of sites in a mountainous basin in south‐central Idaho (Big Wood River Basin). The elevation of the snowline, obtained from the SCA estimates, was used to constrain the predicted SWE values. The results from the analysis are encouraging and compare well to those found in previous studies, which often utilized more sophisticated spatial interpolation techniques. Cross‐validation results indicate that the spatial interpolation method produces accurate SWE estimates [mean R² = 0·82, mean mean absolute error (MAE) = 4·34 cm, mean root mean squared error (RMSE) = 5·29 cm]. The basin examined in this study is typical of many mid‐elevation mountainous basins throughout the western United States, in terms of the distribution of topographic variables, as well as the number and characteristics of sites at which the necessary ground data are available. Thus, there is high potential for this methodology to be successfully applied to other mountainous basins. Copyright © 2010 John Wiley & Sons, Ltd.     

Evaluating best evaporation estimate model for water surface evaporation in semi‐arid region,India

Evaluating performances of four commonly used evaporation estimate methods, namely; Bowen ratio energy balance (BREB), mass transfer (MT), Priestley–Taylor (PT) and pan evaporation (PE), based on 4 years experimental data, the most effective and the reliable evaporation estimates model for the semi‐arid region of India has been derived. The various goodness‐of‐fit measures, such as; coefficient of determination (R²), index of agreement (D), root mean square error (RMSE), and relative bias (RB) have been chosen for the performance evaluation. Of these models, the PT model has been found most promising when the Bowen ratio, β is known a priori, and based on its limited data requirement. The responses of the BREB, the PT, and the PE models were found comparable to each other, while the response of the MT model differed to match with the responses of the other three models. The coefficients, β of the BREB, µ of the MT, α of the PT and K_P of the PE model were estimated as 0·07, 2·35, 1·31 and 0·65, respectively. The PT model can successfully be extended for free water surface evaporation estimates in semi‐arid India. A linear regression model depicting relationship between daily air and water temperature has been developed using the observed water temperatures and the corresponding air temperatures. The model helped to generate unrecorded water temperatures for the corresponding ambient air temperatures. Copyright © 2007 John Wiley & Sons, Ltd.     

Fine Particles (PM2.5) in Residential Areas of Lucknow City and Factors Influencing the Concentration

Urban populations are exposed to a high level of fine and ultrafine particles from motor vehicle emissions which affect human health. To assess the hourly variation of fine particle (PM_2.5) concentration and the influence of temperature and relative humidity (RH) on the ambient air of Lucknow city, monitoring of PM_2.5 along with temperature and RH was carried out at two residential locations, namely Vikas Nagar and Alambagh, during November 2005. The 24 h mean PM_2.5 concentration at Alambagh was 131.74 μg/m³ and showed an increase of 13.74%, which was significantly higher (p < 0.05) than the Vikas Nagar level. The 24 h mean PM_2.5 on weekdays for both locations was found to be 142.74 μg/m³ (an increase of 66.23%) which was significantly higher (p < 0.01) than the weekend value, indicating that vehicular pollution is one of the important sources of PM_2.5. The mean PM_2.5 at night for all the monitoring days was 157.69 μg/m³ and was significantly higher (p < 0.01) than the daytime concentration (89.87 μg/m³). Correlation and multiple regressions showed that the independent variables, i. e., time, temperature, and RH together accounted for 54%, whereas RH alone accounted for 53% of total variations of PM_2.5, suggesting that RH is the best influencing variable to predict the PM_2.5 concentration in the urban area of Lucknow city. The 24 h mean PM_2.5 for all the monitoring days was found to be higher than the NAAQS recommended by the US‐EPA (65 μg/m³) and can be considered to be an alarming indicator of adverse health effects for city dwellers.     

A meteorological-based crop coefficient model for estimation of daily evapotranspiration

Estimation of daily evapotranspiration (ET) over cloudy regions highly desires models which rely on meteorological data only. Notwithstanding, the conventional crop coefficient (K_c) method requires detailed knowledge of geo/biophysical properties of the coupled land-vegetation system, precipitation, and soil moisture. Six Eddy Covariance (EC) towers in Iowa, California and New Hampshire of the USA (covering corn, soybeans, prairie, and deciduous forest) were selected. Investigation on 6 years (2007–2012) 15-min micrometeorological records of these sites revealed that there is an indubitable strong interaction between relative humidity (RH), reference ET (ET_o), and actual ET at different timescales. This allowed to bypass the need for the non-meteorological inputs and express K_c as a second-order polynomial function of RH and ET_o, the ambient regression evapotranspiration model (AREM). The coefficients of the empirical function are crop-specific and may require calibration over different soil types. The mean absolute percentage error (MAPE) of the regression against daily EC observations was 17% during the growing season, and 32% throughout the year with root mean square error (RMSE) of 0.74 mm day⁻¹ and coefficient of determination of 0.71. The model was fully operational (MAPE of 34% and RMSE of 0.82 mm day⁻¹) over the four Iowan sites based on inputs from local weather stations and NLDAS-2 forcing data of NASA. AREM was capable of capturing the dynamics of ET at 15-min and daily timescales irrespective of varying complexities associated with biophysical, geophysical and climatological states.     

Integrating hydrometeorological information for rainfall‐runoff modelling by artificial neural networks

The major purpose of this study is to effectively construct artificial neural networks‐based multistep ahead flood forecasting by using hydrometeorological and numerical weather prediction (NWP) information. To achieve this goal, we first compare three mean areal precipitation forecasts: radar/NWP multisource‐derived forecasts (P_r), NWP precipitation forecasts (P_n), and improved precipitation forecasts (P_m) by merging P_r and P_n. The analysis shows that the accuracy of P_m is higher than that of P_r and P_n. The analysis also indicates that the NWP precipitation forecasts do provide relative effectiveness to the merging procedure, particularly for forecast lead time of 4–6 h. In sum, the merged products performed well and captured the main tendency of rainfall pattern. Subsequently, a recurrent neural network (RNN)‐based multistep ahead flood forecasting techniques is produced by feeding in the merged precipitation. The evaluation of 1–6‐h flood forecasting schemes strongly shows that the proposed hydrological model provides accurate and stable flood forecasts in comparison with a conventional case, and significantly improves the peak flow forecasts and the time‐lag problem. An important finding is the hydrologic model responses which do not seem to be sensitive to precipitation predictions in lead times of 1–3 h, whereas the runoff forecasts are highly dependent on predicted precipitation information for longer lead times (4–6 h). Overall, the results demonstrate that accurate and consistent multistep ahead flood forecasting can be obtained by integrating predicted precipitation information into ANNs modelling. Copyright © 2009 John Wiley & Sons, Ltd.     

Measuring water availability with limited ground data: assessing the feasibility of an entirely remote‐sensing‐based hydrologic budget of the Rufiji Basin,Tanzania, using TRMM,GRACE, MODIS,SRB, and AIRS

This study explores the feasibility of an entirely satellite remote sensing (RS)‐based hydrologic budget model for a ground data‐constrained basin, the Rufiji basin in Tanzania, from the balance of runoff (Q), precipitation (P), storage change (ΔS), and evapotranspiration (ET). P was determined from the Tropical Rainfall Measuring Mission, ΔS from the Gravity Recovery and Climate Experiment, and ET from the Moderate Resolution Imaging Spectroradiometer, the surface radiation budget, and the Atmosphere Infrared Radiation Sounder. Q was estimated as a residual of the water balance and tested against measured Q for a sub‐basin of the Rufiji (the Usangu basin) where ground measurements were available (R² = 0.58, slope = 1.9, root mean square error = 29 mm/month, bias = 14%). We also tested a geographical information system (GIS)‐driven (ArcCN‐runoff) runoff model (R² = 0.64, slope = 0.43, root mean square error = 39 mm/month). We conducted an error propagation analysis from each of the model's hydrologic components (P, ET, and ΔS). We find that the RS‐based model amplitude is most sensitive to ET and slightly less so to P, whereas the model's seasonal trends are most sensitive to ?S. Although RS–GIS‐driven models are becoming increasingly used, our results indicate that long‐term water resource assessment policy and management may be more appropriate than ‘instantaneous’ or short‐term water resource assessment. However, our analyses help develop a series of tools and techniques to progress our understanding of RS–GIS in water resource management of data‐constrained basins at the level of a water resource manager. Copyright © 2012 John Wiley & Sons, Ltd.     

Horizontal and vertical fluxes of particulate matter during wind erosion on arable land in the province La Pampa,Argentina

A detailed analysis of horizontal and vertical particulate matter (PM) fluxes during wind erosion has been done, based on measurements of PM smaller than 10, 2.5, and 1.0 μm, at windward and leeward positions on a measuring field. The three fractions of PM measurement are differently influenced by the increasing wind and shear velocities of the wind. The measured concentrations of the coarser fractions of the fine dust, PM₁₀, and PM_2.5, increase with wind and shear velocity, whereas the PM_1.0 concentrations show no clear correlation to the shear velocity. The share of PM_2.5 on PM₁₀ depends on the measurement height and wind speed and varies between 4 and 12 m/s at the 1 m height ranging from 25% to 7% (average 10%), and at the 4 m height from 39% to 23% (average 30%). Although general relationships between wind speed, PM concentration, and horizontal and vertical fluxes could be found, the contribution of the measuring field was very low, as balances of incoming and outgoing fluxes show. Consequently, the measured PM concentrations are determined from a variety of sources, such as traffic on unpaved roads, cattle drives, tillage operations, and wind erosion, and thus, represent all components of land use and landscape structure in the near and far surroundings of the measuring field. The current results may reflect factors from the landscape scale rather than the influence of field-related variables. The measuring devices used to monitor PM concentrations showed differences of up to 20%, which led to considerable deviations when determining total balances. Differences up to 67% between the calculated fluxes prove the necessity of a previous calibration of the devices used.     

Atmospheric Lead in PM2.5 after Leaded Gasoline Phase‐out in Jeddah City,Saudi Arabia

The objective of the present study is the assessment of Jeddah ambient air quality in terms of PM_2.5, and the associated lead 7 years after phasing out leaded gasoline in Saudi Arabia. Twenty‐four air samples were collected at four locations throughout Jeddah during the period from December 23, 2008 to April 6, 2009. The collected PM_2.5‐samples were analyzed by ICP‐MS for determination of lead. The average atmospheric PM_2.5 concentration was 50.8 µg/m³. Atmospheric PM_2.5‐concentrations were higher than the 24‐h U.S. National Ambient Air Quality Standards (NAAQS) in 14 sample events. The average lead concentration for all samples was 0.07326 µg/m³. Atmospheric lead concentration was dependent on the sampling location. Concentrations at the two southern locations were higher than at the two northern locations. Southern locations had higher lead concentrations due to very high traffic density, in addition to their proximity to industrial zone. In general, the results of this study show a considerable decrease in atmospheric lead concentration 7 years after phasing out leaded gasoline. The study recommends further studies to accurately determine the current sources of atmospheric lead.     

Short‐term inflow forecasting using an artificial neural network model

The primary objective of this study is to investigate the possibility of including more temporal and spatial information on short‐term inflow forecasting, which is not easily attained in the traditional time‐series models or conceptual hydrological models. In order to achieve this objective, an artificial neural network (ANN) model for short‐term inflow forecasting is developed and several issues associated with the use of an ANN model are examined in this study. The formulated ANN model is used to forecast 1‐ to 7‐h ahead inflows into a hydropower reservoir. The root‐mean‐squared error (RMSE), the Nash–Sutcliffe coefficient (NSC), the A information criterion (AIC), B information criterion (BIC) of the 1‐ to 7‐h ahead forecasts, and the cross‐correlation coefficient between the forecast and observed inflows are estimated. Model performance is analysed and some quantitative analysis is presented. The results obtained are satisfactory. Perceived strengths of the ANN model are the capability for representing complex and non‐linear relationships as well as being able to include more information in the model easily. Although the results obtained may not be universal, they are expected to reveal some possible problems in ANN models and provide some helpful insights in the development and application of ANN models in the field of hydrology and water resources. Copyright © 2002 John Wiley & Sons, Ltd.