Combination Of Meteorological Data Research Articles

Optimization Model for Determining Global Solar Radiation in the Northeastern States of Nigeria Using Both Meteorological and Satellite Imagery Data

This study presents an optimization model for determining global solar radiation in the northeastern region of Nigeria using a combination of meteorological data and satellite imagery. Ten recent models were chosen from the literature review and optimized to select the one that best fits the study region. Two models were developed to provide accurate solar radiation predictions, which can be used to improve the planning and implementation of a solar energy project in the region. The model integrates the Angstrom-Prescott model with various climate parameters such as Temperature (∆T), relative humidity (RH), location latitude (Φ), solar declination angle (δ), and the number of days in a year (n) with satellite image data to determine the global solar radiation. The finding of optimization models shows that the model10 performed very well with minimum error as Mean Base Error (0.028), Mean Percentage Error (-0.001), Root Mean Square Error (0.098), and coefficient of determination R2 (0.994), which suggested as the optimized model for determining of global solar radiation in northeastern Nigeria. The two models were developed, that is, proposed Model1 and proposed Model2. Proposed Model1 slightly overestimated the global solar radiation with Mean Base Error (-0.863), Mean Percentage Error (-0.039), Root Mean Square Error (2.990), and coefficient of determination R2 (0.745), while proposed Model2 performed better with Mean Base Error (-0.005), Mean Percentage Error (0.0003), Root Mean Square Error (0.02) with the coefficient of determination R2 (0.985). The proposed models were validated using the suggested optimized model10 and satellite data model, which show that the proposed model can accurately determine global solar radiation in the northeastern region of Nigeria. This study's findings will benefit the region's solar energy project developers, researchers, and policymakers.

Artificial neural network and energy budget method to predict daily evaporation of Boudaroua reservoir (northern Morocco)

Evaporation is one of the main essential components of the hydrologic cycle. The study of this parameter has significant consequences for knowing reservoir level forecasts and water resource management. This study aimed to test the three artificial neural networks (feed-forward, Elman and nonlinear autoregressive network with exogenous inputs (NARX) models) and multiple linear regression to predict the rate of evaporation in the Boudaroua reservoir using the calculated values obtained from the energy budget method. The various combinations of meteorological data, including solar radiation, air temperature, relative humidity, and wind speed, are used for the training and testing of the model’s studies. The architecture that was finally chosen for three types of neural networks has the 4-10-1 structure, with contents of 4 neurons in the input layer, 10 neurons in the hidden layer and 1 neuron in the output layer. The calculated evaporation rate presents a typical annual cycle, with low values in winter and high values in summer. Moreover, air temperature and solar radiation were identified as meteorological variables that mostly influenced the rate of evaporation in this reservoir, with an annual average equal to 4.67 mm∙d –1. The performance evaluation criteria, including the coefficient of determination (R 2), root mean square error ( RMSE) and mean absolute error ( MAE) approved that all the networks studied were valid for the simulation of evaporation rate and gave better results than the multiple linear regression (MLR) models in the study area.

Modelling of Reference Evapotranspiration using Neural Network and Regression Approaches for Semi-humid Region of Sikkim

This study compared the applicability of multiple linear regression (MLR) and artificial neural network (ANN) approaches for estimating the weekly reference evapotranspiration (ET0) in a semi-humid area of Tadong in Gangtok district of Sikkim state in India. Daily meteorological data (1991 - 2020) were used for developing MLR and ANN model using various combinations of meteorological data. Predicted ET0 values were compared with the FAO-56 Penman Monteith (PM) equation estimated ET0. The coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and Nash Sutcliffe efficiency (NSE) were used to assess the performance of MLR and ANN models. For ANN model, one hidden layer and four neurons was found as the best ANN architecture. ANN-4 model with inputs parameters of maximum air temperature (Tmax), minimum air temperature (Tmin), maximum relative humidity (RHmax), minimum relative humidity (RHmin), and sunshine hours (SSH) had the smallest RMSE (0.609 mm.day-1 and 0.880 mm.day-1), MAE (0.473 mm.day-1 and 0.551mm.day-1) and the highest R2 (0.752 and 0.657) and NSE (0.782 and 0.696) during training and testing phases, respectively. The MLR-4 model with same combination of input parameters (Tmax, Tmin, RHmax, RHmin, SSH) resulted in smallest RMSE (0.704 mm.day-1 and 0.714 mm.day-1), MAE (0.562 mm.day-1and 0.583 mm.day-1); and the highest R2 (0.679 and 0.602) and NSE (0.708 and 0.597) during training and testing phase, respectively. Study also showed increase in model performance of ANN and MLR models with increase in number of meteorological parameters. All the ANN models gave comparable results, but ANN 4 model resulted in higher prediction accuracy for the sub-humid region of Tadong in Gangtok district of North-east India. The developed ANN-4 model could be helpful for preparing irrigation schedules and planning, and management of water resources in the data scarce northeast region of India and other identical climatic regions.

Hybrid Genetic Algorithm−Based BP Neural Network Models Optimize Estimation Performance of Reference Crop Evapotranspiration in China

Precise estimation of reference evapotranspiration (ET0) is of significant importance in hydrologic processes. In this study, a genetic algorithm (GA) optimized back propagation (BP) neural network model was developed to estimate ET0 using different combinations of meteorological data across various climatic zones and seasons in China. Fourteen climatic locations were selected to represent five major climates. Meteorological datasets in 2018–2020, including maximum, minimum and mean air temperature (Tmax, Tmin, Tmean, °C) and diurnal temperature range (∆T, °C), solar radiation (Ra, MJ m−2 d−1), sunshine duration (S, h), relative humidity (RH, %) and wind speed (U2, m s−1), were first subjected to correlation analysis to determine which variables were suitable as input parameters. Datasets in 2018 and 2019 were utilized for training the models, while datasets in 2020 were for testing. Coefficients of determination (r2) of 0.50 and 0.70 were adopted as threshold values for selection of correlated variables to run the models. Results showed that U2 had the least r2 with ET0, followed by ∆T. Tmax had the greatest r2 with ET0, followed by Tmean, Ra and Tmin. GA significantly improved the performance of BP models across different climatic zones, with the accuracy of GABP models significantly higher than that of BP models. GABP0.5 model (input variables based on r2 > 0.50) had the best ET0 estimation performance for different seasons and significantly reduced estimation errors, especially for autumn and winter seasons whose errors were larger with other BP and GABP models. GABP0.5 model using radiation/temperature data is highly recommended as a promising tool for modelling and predicting ET0 in various climatic locations.

Coupling meteorological stations data and satellite data for prediction of global solar radiation with machine learning models

The simulation of solar radiation is of great significance to the sustainable development of energy, engineering, and many other fields. The Himawari series of satellites has the characteristics of high temporal, spatial resolution, which helps to solve the problem of insufficient ground radiation observation in China. However, the accuracy of this data needs to be further improved. Thus, four machine learning models with 13 ground and satellite-based input combinations were used to simulate daily solar radiation. The results showed that the simulation accuracy of the model based on a combination of meteorological data from different sources was significantly improved compared with the model based on single-source data. The RMSE was 32.4% and 44.6% lower than those of the model based on the ground meteorological stations data and the model based on the satellite data, respectively. SVM13 model showed the optimal simulation performance compared with other models, and its RMSE and R2 were 1.732 MJ m−2 day−1 and 0.939 in each climate region, respectively. Overall, we conclude that the SVM13 model is the most suitable model, and the model with a complex combination of more meteorological factors as input has higher simulation accuracy than the model with a relatively simple input combination.

Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction

Solar energy serves as a great alternative to fossil fuels as they are clean and renewable energy. Accurate solar radiation (SR) prediction can substantially lower down the impact cost pertaining to the development of solar energy. Lately, many SR forecasting system has been developed such as support vector machine, autoregressive moving average and artificial neural network (ANN). This paper presents a comprehensive study on the meteorological data and types of backpropagation (BP) algorithms used to train and develop the best SR predicting ANN model. The meteorological data, which includes temperature, relative humidity and wind speed are collected from a meteorological station from Kuala Terrenganu, Malaysia. Three different BP algorithms are employed into training the model i.e., Levenberg–Marquardt, Scaled Conjugate Gradient and Bayesian Regularization (BR). This paper presents a comparison study to select the best combination of meteorological data and BP algorithm which can develop the ANN model with the best predictive ability. The findings from this study shows that temperature and relative humidity both have high correlation with SR whereas wind temperature has little influence over SR. The results also showed that BR algorithm trained ANN models with maximum R of 0.8113 and minimum RMSE of 0.2581, outperform other algorithm trained models, as indicated by the performance score of the respective models.

Analysis of Ozone Pollution Spatio-temporal Evolution Characteristics and Identification of Its Long-term Variation Driving Factor over Hunan Province

Despite the alleviation of particulate matter (PM), the ambient ozone (O3) concentration is continuously increasing in Hunan province where the investigation of O3 pollution has been rarely reported. Accordingly, the spatio-temporal evolution of O3 pollution was first analyzed based on hourly air quality data observed by national monitoring stations from 2015 to 2020 over 14 cities in Hunan province. Afterwards, the combination of meteorological data from the European Center for Medium-range Weather Forecast (ECMWF) and the generalized additive model (GAM) was applied to investigate the driving factors of the O3 long-term trend during this period. The results presented obvious diurnal, monthly, and seasonal characteristics of O3 variations. High O3 concentrations occurred in May and September monthly, and the peak O3 season was autumn. Furthermore, the 90th percentile O3 increased at a rate of 4.7 μg·(m3·a)-1 temporally, and high O3 values mainly occurred in the north-eastern region spatially, in contrast to the low O3 values in the western region. The modeling results indicated that the increase in O3 was mainly ascribed to precursor emissions. Furthermore, meteorology promoted a rise in O3 with the impact magnitude of 1 μg·(m3·a)-1. Remarkably, meteorology accelerated the O3 increases in spring, summer, and the eastern region, whereas it restrained increases in autumn, winter, and the northwest. The effect of meteorology on PM10 was different from O3 during this period. Overall, this study highlighted the importance of meteorological impacts when regulating emission reduction measures for O3 abatement. It required greater effort regarding O3 mitigation to offset the side-effect from meteorology in meteorology-sensitive seasons and regions. Additionally, the regional corporation is indispensable to reduce O3 transportation from upwind.

Modelling the reference crop evapotranspiration in the Beas-Sutlej basin (India): an artificial neural network approach based on different combinations of meteorological data.

Accurate prediction of the reference evapotranspiration (ET0) is vital for estimating the crop water requirements precisely. In this study, we developed multi-layer perceptron artificial neural network (MLP-ANN) models considering different combinations of the meteorological data for predicting the ET0 in the Beas-Sutlej basin of Himachal Pradesh (India). Four climatic locations in the basin namely, Kullu, Mandi, Bilaspur, and Chaba were selected. The meteorological dataset comprised air temperature (maximum, minimum and mean), relative humidity, solar radiation, and wind speed, recorded daily for a period of 35years (1984-2019). The datasets from 1984 to 2012 and 2013 to 2019 were utilized for training and testing the models, respectively. The performance of the developed models was evaluated using several statistical indices. For each location, the best performed MLP-ANN model was the one with the complete combination of the meteorological data. The architecture of the best performing model for Kullu, Mandi, Bilaspur, and Chaba was (6-2-4-1), (6-5-4-1), (6-5-4-1), and (6-4-6-1), respectively. It was observed, however, that the performance of other models was also relatively good, given the limited meteorological data utilized in those models. Further, to appreciate the relative predictive ability of the developed models, a comparison was performed with four existing established empirical models. The approach adopted in this study can be effectively utilized by water users and field researchers for modelling and predicting ET0 in data-scarce locations.

Long-term variations in actual evapotranspiration over the Tibetan Plateau

Abstract. Actual terrestrial evapotranspiration (ETa) is a key parameter controlling land–atmosphere interaction processes and water cycle. However, spatial distribution and temporal changes in ETa over the Tibetan Plateau (TP) remain very uncertain. Here we estimate the multiyear (2001–2018) monthly ETa and its spatial distribution on the TP by a combination of meteorological data and satellite products. Validation against data from six eddy-covariance monitoring sites yielded root-mean-square errors ranging from 9.3 to 14.5 mm per month and correlation coefficients exceeding 0.9. The domain mean of annual ETa on the TP decreased slightly (−1.45 mm yr−1, p<0.05) from 2001 to 2018. The annual ETa increased significantly at a rate of 2.62 mm yr−1 (p<0.05) in the eastern sector of the TP (long >90∘ E) but decreased significantly at a rate of −5.52 mm yr−1 (p<0.05) in the western sector of the TP (long <90∘ E). In addition, the decreases in annual ETa were pronounced in the spring and summer seasons, while almost no trends were detected in the autumn and winter seasons. The mean annual ETa during 2001–2018 and over the whole TP was 496±23 mm. Thus, the total evapotranspiration from the terrestrial surface of the TP was 1238.3±57.6 km3 yr−1. The estimated ETa product presented in this study is useful for an improved understanding of changes in energy and water cycle on the TP. The dataset is freely available at the Science Data Bank (https://doi.org/10.11922/sciencedb.t00000.00010; Han et al., 2020b) and at the National Tibetan Plateau Data Center (https://doi.org/10.11888/Hydro.tpdc.270995, Han et al., 2020a).

Comparison of four bio-inspired algorithms to optimize KNEA for predicting monthly reference evapotranspiration in different climate zones of China

Accurate estimation of reference crop evapotranspiration (ET0) is of great significance to crop water use and agricultural water resources management. This study evaluated the performance of four bio-inspired algorithm optimized kernel-based nonlinear extension of Arps decline (KNEA) models, namely KNEA with grasshopper optimization algorithm (GOA-KNEA), KNEA with grey wolf optimizer algorithm (GWO-KNEA), KNEA with particle swarm optimization algorithm (PSO-KNEA), and KNEA with salp swarm algorithm (SSA-KNEA), on estimating monthly ET0 across China. Monthly meteorological data [including maximum air temperature (Tmax), minimum air temperature (Tmin), extra-terrestrial solar radiation (Ra), relative humidity (RH), global solar radiation (Rs), wind speed (U)] during 1966–2015 from 51 weather stations across the seven different climate zones of China were used for model training and testing. Four different combinations of meteorological data were applied as model input, and results from the FAO-56 Penman-Monteith formula were used as a control. Results showed that the GWO-KNEA model overall performed better than the other three coupling models, of which the GWO-KNEA2 model (i.e., the model with input combination 2) was the best (on average R2 = 0.9814, RMSE = 0.2143 mm d−1). The convergence rate and the population size of the GWO-KNEA model were also superior to the other three models. Among input combinations, models with combination 2 had the best overall performance, while models with combination 3 were the worst on average. In terms of the importance of each meteorological parameter contributing to model accuracy, Rs was greater than Ra, RH, or U. Among different climate zones, station specific models in the semi-arid steppe of Inner Mongolia showed the best estimating performance in general, while models in the Qinghai-Tibetan Plateau overall performed relatively poorly. The GOA-KNEA and SSA-KNEA models with the combination 4 showed large increases (29.7% and 28.7%, respectively), indicating a problem of overfitting. Conversely, the GWO-KNEA model was the most stable model with the smallest increase in RMSE during the testing phase (4.4–15.7%) among all models. In summary, taking the model's estimation accuracy, stability, convergence rate, and climate environment into account, our results suggest that GWO-KNEA would be a suitable model for estimating ET0 in different climate zones of China and the most practical input combination would be combination 2.

Observations of an Extreme Atmospheric River Storm With a Diverse Sensor Network

AbstractObservational networks enhance real‐time situational awareness for emergency and water resource management during extreme weather events. We present examples of how a diverse, multitiered observational network in California provided insights into hydrometeorological processes and impacts during a 3‐day atmospheric river storm centered on 14 February 2019. This network, which has been developed over the past two decades, aims to improve understanding and mitigation of effects from extreme storms influencing water resources and natural hazards. We combine atmospheric reanalysis output and additional observations to show how the network allows: (1) the validation of record cool season precipitable water observations over southern California; (2) the identification of phenomena that produce natural hazards and present difficulties for short‐term weather forecast models, such as extreme precipitation amounts and snow level variability; (3) the use of soil moisture data to improve hydrologic model forecast skill in northern California's Russian River basin; and (4) the combination of meteorological data with seismic observations to identify when a large avalanche occurred on Mount Shasta. This case study highlights the value of investments in diverse observational assets and the importance of continued support and synthesis of these networks to characterize climatological context and advance understanding of processes modulating extreme weather.

Using Multi-Temporal MODIS NDVI Data to Monitor Tea Status and Forecast Yield: A Case Study at Tanuyen, Laichau, Vietnam

Tea is a cash crop that improves the quality of life for people in the Tanuyen District of Laichau Province, Vietnam. Tea yield, however, has stagnated in recent years, due to changes in temperature, precipitation, the age of the tea bushes, and diseases. Developing an approach for monitoring tea bushes by remote sensing and Geographic Information Systems (GIS) might be a way to alleviate this problem. Using multi-temporal remote sensing data, the paper details an investigation of the changes in tea health and yield forecasting through the normalized difference vegetation index (NDVI). In this study, we used NDVI as a support tool to demonstrate the temporal and spatial changes in NDVI through the extract tea NDVI value and calculate the mean NDVI value. The results of the study showed that the minimum NDVI value was 0.42 during January 2013 and February 2015 and 2016. The maximum NDVI value was in August 2015 and June 2017. We indicate that the linear relationship between NDVI value and mean temperature was strong with R 2 = 0.79 Our results confirm that the combination of meteorological data and NDVI data can achieve a high performance of yield prediction. Three models to predict tea yield were conducted: support vector machine (SVM), random forest (RF), and the traditional linear regression model (TLRM). For period 2009 to 2018, the prediction tea yield by the RF model was the best with a R 2 = 0.73 , by SVM it was 0.66, and 0.57 with the TLRM. Three evaluation indicators were used to consider accuracy: the coefficient of determination ( R 2 ), root-mean-square error (RMSE), and percentage error of tea yield (PETY). The highest accuracy for the three models was in 2015 with a R 2 ≥ 0.87, RMSE < 50 kg/ha, and PETY less 3% error. In the other years, the prediction accuracy was higher in the SVM and RF models. Meanwhile, the RF algorithm was better than PETY (≤10%) and the root mean square error for this algorithm was significantly less (≤80 kg/ha). RMSE and PETY showed relatively good values in the TLRM model with a RMSE from 80 to 100 kg/ha and a PETY from 8 to 15%.

Estimation of Actual Evapotranspiration in Fragmented Mediterranean Areas by the Spatio-Temporal Fusion of NDVI Data

Actual evapotranspiration $(\text{ET}_{{\rm{A}}})$ is a fundamental component of the land water cycle that can be predicted by the combination of meteorological data and remotely sensed normalized difference vegetation index (NDVI) observations. The proficient application of this approach to the retrospective study of fragmented areas, however, depends on the preliminary use of spatio-temporal fusion (STF) methods capable of integrating different satellite datasets. One of these methods is the Spatial Enhancer of Vegetation Index image Series (SEVIS), which has been recently developed to improve the annual NDVI datasets based on one or a few high spatial resolution images. This STF method is currently applied to moderate resolution imaging spectroradiometer (MODIS) and TM/ETM+/OLI imagery taken over three fragmented areas in Tuscany (Central Italy), representative of different Mediterranean ecosystems, i.e., an urban grassland, a tomato field, and an olive grove. The performance of SEVIS is evaluated by comparing the $\text{ET}_{{\rm{A}}}$ estimates obtained from the original (MODIS) and synthetic (MODIS plus TM/ETM+/OLI) NDVI datasets to ground $\text{ET}_{{\rm{A}}}$ observations. The experimental results indicate that the original MODIS NDVI data cannot properly characterize the seasonal vegetation evolutions of the three study sites, which negatively affects the performance of $\text{ET}_{{\rm{A}}}$ simulation. In contrast, such evolutions are reasonably reproduced by the synthetic NDVI datasets, which improves the accuracy of the $\text{ET}_{{\rm{A}}}$ estimates both in terms of correlation and errors. The improvements are particularly evident during the summer dry period when the MODIS images are incapable of characterizing the actual vegetation response to water stress.

Comparison of support vector machine and copula-based nonlinear quantile regression for estimating the daily diffuse solar radiation: A case study in China

In this paper, three kinds of models, including support vector machine-firefly algorithm (SVM-FFA), copula-base nonlinear quantile regression (CNQR) and empirical models were developed for daily diffuse radiation (Hd) estimation. The meteorological data during 1981–2000 and 2001–2010 of Lhasa, Urumqi, Beijing and Wuhan in China were used for model training and validation, respectively. Five combinations of meteorological data: (a) clearness index (Kt); (b) sunshine ratio (S); (c) Kt and S; (d) Kt, S and average temperature (Ta); (e) Kt, S, Ta and average relative humidity, were considered for simulation. The results showed that for the training phases, SVM-FFA outperformed the corresponding models while empirical models performed slightly better than corresponding CNQR models. For validation phases, CNQR and SVM-FFA models performed much better than empirical models. Compared CNQR and SVM-FFA, SVM-FFA performed slightly better than CNQR models with average MABE decreased by 0.67% (0.01 MJm−2d−1) and average R2 increased by 0.43% (0.004). For the training time, SVM-FFA (1.68 s) showed less computational costs than CNQR (6.68 s); but the parameter optimization time of SVM-FFA (4.9 × 105) were 105 times as much as CNQR. Thus, the overall computational costs of SVM-FFA during training phases were much higher than CNQR. Considering the trade-off between accuracy and computational costs, CNQR were highly recommended for the daily Hd estimation.

Identification of industrial point sources of airborne dust particles in an urban environment by a combined mineralogical and meteorological analyses: A case study from the Upper Silesian conurbation, Poland

Specific industrial point sources of PM10 were identified by combining real-time meteorological observations and mineralogical examination (XRD, SEM-EDS, TEM-EDS) of individual particles collected during five individual week-long sampling campaigns in 2011 in the most industrialized region of Poland. Major (>20 vol%) and subordinate components of PM10 (soot, quartz, gypsum, aluminosilicates, Fe-oxides, dolomite, and Pb-chloride) occur in different proportions season-depending and are of limited use in the identification of the emission point sources. Some minor components (particles of Cd- and Tl-bearing ZnS, and of iron and Cr-steel) related to meteorological conditions enabled identification of a large zinc refinery (35 km NW of the sampling site) and integrated iron and steelmaking plant located close to (10 km NE) the sampling site as the most probable emission point sources. Three other large steel plants located in the SW wind rose sector at a distance of 28, 75, and 80 km of the sampling site were typified as potential point sources based on submicron metal particles (Mn-alloyed steel, brass, hematite, magnetite, Fe-spinels). Combination of meteorological data and mineralogical investigation of airborne individual particles enabled distinguishing between sources of similar mineral tracers located in different sectors of wind rose and provided direct evidence for Zn and Cl speciation.

Simulation of Soil Water Content in Mediterranean Ecosystems by Biogeochemical and Remote Sensing Models

The current study assesses the potential of two modeling approaches to simulate the daily site water budget in Mediterranean ecosystems. Both models utilize a simplified one-bucket approach but are fed with different drivers. The first model, BIOME-BGC, simulates all main biogeochemical fluxes based on conventional meteorological and ancillary data, while the second uses evapotranspiration estimates derived from the combination of meteorological data and satellite normalized difference vegetation index (NDVI) images. The two models were tested for three Italian sites which are characterized by different vegetation types and ecoclimatic conditions: (i) low mountain coniferous forest; (ii) hilly deciduous forest; (iii) urban grassland. The soil water balance simulated by the two models was evaluated through comparison with daily measurements of soil water content (SWC) taken during a growing season. Satisfactory results were obtained in all cases by both approaches; the SWC estimates are significantly correlated with the measurements (correlation coefficient, r, higher than 0.74), and the mean errors are lower than 0.079 cm3 cm−3. The second model, however, generally shows a higher accuracy, which is dependent on the quality of the NDVI data utilized (r higher than 0.79 and errors lower than 0.059 cm3 cm−3). The study therefore provides useful indications for the application of these and similar simulation methods in different environmental situations.

Testing approaches for calculating stomatal ozone fluxes from passive samplers

Current ozone (O3) levels are high enough to negatively affect vegetation and may become worse in the future. Ozone risk assessments have recently shifted from exposure-based to flux-based metrics. Modeling stomatal O3 fluxes requires hourly O3 and meteorological data, which are not always available. Large datasets of O3 concentrations measured with passive samplers exist worldwide, and usually provide weekly to monthly means. We tested the suitability of using aggregated data instead of hourly data for O3 flux calculations with 3-year time series of O3 data from 24 Spanish air quality stations. Five different approaches and three different parameterizations were tested. Ozone-averaged values in combination with hourly meteorological data provided the most robust estimates of accumulated O3 fluxes (Phytotoxic Ozone Dose with no threshold, POD0), and the median of the absolute percent error (MAPE) due to aggregation came close to 5%. Aggregations from 1week to 1month yielded similar errors, which is important in the cost-efficiency terms of the chosen passive sampler exposure periodicity. One major limitation of these approaches is that they are not suitable for high POD thresholds, and that accuracy of the measurements with passive samplers has to be strictly assured in order to finally obtain acceptable errors. A combination of meteorological data and O3 passive sampler measurements may be used to estimate O3 fluxes at remote forest sites as a valuable risk assessment tool.

Spatial-temporal variation of groundwater and land subsidence evolution in Beijing area

Abstract. Precipitation is the main recharge source of groundwater in the plain of Beijing, China. Rapid expansion of urbanization has resulted in increased built-up area and decreased amount of effective recharge of precipitation to groundwater, indirectly leading to the long-term over-exploitation of groundwater, and induced regional land subsidence. Based on the combination of meteorological data, groundwater level data, interferometric synthetic aperture radar (InSAR; specifically persistent scatterer interferometry, PSI), geographic information system (GIS) spatial analysis method and rainfall recharge theory, this paper presents a systematic analysis of spatial-temporal variation of groundwater level and land subsidence evolution. Results show that rainfall has been decreasing annually, while the exploitation of groundwater is increasing and the groundwater level is declining, which is has caused the formation and evolution of land subsidence. Seasonal and interannual variations exist in the evolution of land subsidence; the subsidence is uneven in both spatial and temporal distribution. In 2011, at the center of mapped subsidence the subsidence rate was greater than 120 mm a−1. The results revealed good correlation between the spatial distribution of groundwater level declines and subsidence. The research results show that it is beneficial to measure the evolution of land subsidence to dynamic variations of groundwater levels by combining InSAR or PSI, groundwater-level data, and GIS. This apprpach provides improved information for environmental and hydrogeologic research and a scientific basis for regional land subsidence control.

Ground/satellite observations and atmospheric modeling of dust storms originating in the high Puna‐Altiplano deserts (South America): Implications for the interpretation of paleo‐climatic archives

AbstractThis study provides a detailed description of the sources, transport, dispersion, and deposition of two major dust events originating from the high‐altitude subtropical Puna‐Altiplano Plateau (15–26°S; 65–69°W) in South America. A long and severe drought provided the right conditions for the onset of both events in July 2009 and 2010. Dust was transported SE and deposited over the Pampas region and was observed to continue to the Atlantic Ocean. Dust monitoring stations located downwind recorded both events, and samples were characterized through chemical and textural analysis. Through a combination of meteorological data and satellite observations (CALIPSO and MODIS detectors), we estimate the emission flux for the 2010 event. This estimate was used to constrain the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) transport model and simulate the dust event. Both satellite imagery and model results agree in the location and extension of the dust cloud. CALIPSO detected dust between ~6000 and ~8500 m a.s.l., which remained at this height during most of its trajectory. The dust cloud mixed with a strong convective system in the region, and the associated precipitation brought down significant amounts of dust to the ground. Dust particle size analysis for both events indicates that near the sources dust samples show median modes of 12.4–14.1 µm, similar to modes observed 1300 km away. Chemical composition of sediments from potential dust sources shows distinct signatures within the Puna‐Altiplano Plateau, the Puna sector being clearly different from the Altiplano area. In addition, both sources are markedly different from the Patagonian chemical fingerprint. These results have important implications to improve the interpretation of paleo‐environmental archives preserved on the Argentine loess, Antarctic ice cores, and Southern Ocean marine sediments.

Identifying Priority Pollutant Sources: Apportioning Air Toxics Risks using Positive Matrix Factorization

Hazardous air pollutants or air toxics are pollutants that are known or suspected to cause serious health effects. This paper presents a methodology to quantify source contributions to air toxics health risks. First, a linear, no-threshold risk model was used to identify gas-phase organic air toxics that contribute significantly to cancer risks. Next, Positive Matrix Factorization (PMF) was performed on high time-resolved measurements of these air toxics, and the additive cancer risks associated with each factor was determined. Finally, the PMF factors were linked to sources and source classes (mobile, nonmobile, secondary/background) using a combination of meteorological data and comparisons with published source profiles. The analysis was performed using data from three sites in Pittsburgh, Pennsylvania: a downtown site near a heavily traveled bus route, a residential site adjacent to a heavily industrialized area, and an urban background site. At all three sites emissions from nonmobile sources were the dominant contributors to the cancer risks from air toxics included in the PMF model, including benzene and other air toxics often associated with mobile source emissions. Emissions from both large industrial sources, such as coke works and chemical facilities, and smaller point sources, such as dry cleaners, contributed significantly to the cancer risks at all sites. This method can provide insight for decision makers to prioritize sources for risk reduction.