The Saudi Arabian economy has reported an unprecedented increase in growth rates over the last few decades. This growth has significantly increased the demand for non-renewable energy, leading to environmental risks. Renewable energy and technological innovation are the most effective instruments for reducing carbon dioxide (CO2) emissions, decreasing energy consumption, and enhancing energy efficiency. This study aims to explore the effects of renewable energy and technological innovation on CO2 emissions in Saudi Arabia over the period 1990–2023. To investigate this nexus, examine the long-run relationship, and determine causal connections, we employ the Autoregressive Distributed Lag model and the Vector Error Correction Model. The findings indicate that renewable energy and technological innovation help reduce CO2 emissions in both the short run and long run, improving environmental quality. Furthermore, the study utilizes several control variables related to information and communication, revealing that internet and mobile phone usage help lower CO2 emissions in both the short run and long run, thereby enhancing environmental quality. Nevertheless, the use of fixed telephones does not affect CO2 emissions. Based on the empirical results, this study provides valuable insights for policymakers to enhance environmental quality.

The Internet of Urban Things (IoUTs) regularly generates large amounts of data, making it a focus of cyberthreats such as denial-of-service attacks and malware bot networks. Traditional intrusion detection systems struggle to detect intricate attack patterns, handle class imbalance, capture temporal dependencies, and exhibit transparency. To address these limitations, we introduce a novel deep machine learning model, DeepSecure, a hybrid model that combines Deep Belief Networks (DBN) for hierarchical feature extraction and Deep Neural Networks for attack classification. DBN is used for feature selection through unsupervised learning to extract hierarchical representations in the IoUTs network data. We assess the random oversampling examples technique to improve model generalization and prevent class imbalance. DeepSecure is implemented using the TON_IoT dataset, which includes multi-source attack data indicative of Industry 4.0 cyber risks. In multi-class classification, DeepSecure shows improvement score of 21.25% in accuracy, recall, F1-score, and 18.29% in precision. Whereas, in binary classification, it increases accuracy by 11.23%, precision by 15.11%, F1-score by 10%, and recall by 5.32%. ANOVA T-test and 10-fold cross-validation are utilized for results validation to ensure DeepSecure's reliability. Additionally, we use Shapley additive explanations to interpret the DeepSecure's decision-making process to provide insight into feature contributions and model transparency. By effectively tackling IoUTs-specific cybersecurity challenges such as attack pattern detection, data imbalance, and lack of interpretability, the results demonstrate that DeepSecure is a practical, and transparent, solution for IoUTs network security.

The global environment has witnessed an increase in environmental risks over the last few decades due to the rising demand for energy to support economic development and urbanization. These environmental risks are exacerbated by the escalating human activity that depletes natural resources. Therefore, analyzing factors affecting Ecological Footprint (EFP), which include many variables such as urbanization, energy consumption, natural resources, economic growth, and technological innovation, is essential to achieve sustainable development. Urbanization is a key driver of economic growth. Achieving economic development requires the utilization of natural resources and energy which increase the EFP. Therefore, the focus on technological innovation is essential to reduce the EFP. Despite the critical environmental and economic implications of factors affecting EFP, studies on this area are lacking, especially across Middle Eastern countries, and present contradictory findings. Therefore, the main aim of this study is to investigate the effect of urbanization, energy consumption, natural resources, economic growth, and technological innovation on the EFP in Saudi Arabia. To this end, the study utilizes an autoregressive distributed lag (ARDL) model, which is considered the most suitable econometric approach when variables are stationary at I (0) or integrated of order I (1), based on data collected from various international sources for the period spanning from 1990 to 2022. In both the long run and the short run, empirical findings show that urbanization, natural resources, and technological innovation decrease the EFP, while energy consumption and economic growth increase the EFP. These results reveal that energy policies need to be addressed, and economic growth is unable to lower the EFP due to a lack of connection between economic policies and environmental goals. On the other hand, the study shows that urban policies and the management of natural resources are effectively linked to environmental goals. These findings have several significant policy implications for reducing the EFP. Suggestions include effectively linking economic policies to environmental goals by electrifying the economy. Additionally, several procedures should be considered, including replacing current carbon-based energy with renewable sources, reevaluating the pricing of the energy system, increasing taxes on carbon-based energy, and reassessing current energy laws and regulations.

Smart governance is a powerful political instrument to enhance the quality of public decision-making. Saudi Arabia has established smart city strategies using Information Communication Technologies to improve the quality of life and facilitate sustainable development. This study assesses smart governance, based on identifying five distinct areas by means of sixteen indicators for smart governance performance. To this end, data was collected from public and international sources, as well as from the literature. The findings reveal that Saudi Arabia has achieved solid progress in this field. Two factors, the e-service system and spending on public services, have highly contributed to this improvement. Other factors, such as citizen participation, governmental organization, and political domain have also played an important role; however, there are still some challenges that need to be addressed.

Abstract The increasing demand for electricity in daily life highlights the need for Smart Cities (SC) to use energy efficiently. Both technical and Non‐Technical Losses (NTL), particularly those resulting from electricity theft, present powerful obstacles; NTL alone can reach billions of dollars. Although Machine Learning (ML) based approaches for NTL detection have been embraced by numerous utilities, there is still a lack of thorough analysis of these methods. Limited research exists on NTL identification evaluation criteria and unbalanced data management in the context of SC. This research compares ML algorithms and data balancing methods to optimize electricity consumption detection. The given research applied the 15 ML techniques of Logistic regression, Bernoulli naive Bayes, Gaussian naive Bayes, K‐Nearest Neighbour, perceptron, passive‐aggressive classifier, quadratic discriminant analysis, SGD classifier, ridge classifier, linear discriminant analysis, decision tree, nearest centroid classifier, multi‐nomial naive Bayes, complement naive Bayes and dummy classifier. While SMOTE, AdaSyn, NRAS, and CCR are considered for data balancing. AUC, F1‐score, and seven relevant performance metrics were used for comparison. We have also implemented SHapely Additive exPlanations (SHAP) for feature importance and model interpretation. Results show varying classifier performance with different balancing methods, emphasizing data preprocessing's role in NTL detection for smart grid security.

One of the crucial issues for power grids in strengthening the urbanization around the world is imbalance between supply and demand, which leads the users to consume electricity in an anomalous manner without paying for it. Electricity theft plays a pivotal role in cutting down on the electricity bills. The existing data-oriented approaches for electricity theft detection (ETD) in the smart cities have limited ability to handle noisy high-dimensional data and features’ associations. These limitations raise the misclassification rate, which makes some of the approaches unacceptable for electric utilities. A new twofold end-to-end methodology is proposed for ETD. In the first fold, it groups the similar electricity consumption (EC) cases through grey wolf optimization (GWO)-based clustering mechanism; clustering by fast search and find of density peaks (CFSFDP), we named it GC. In the second fold, a new relational stacked denoising autoencoder (RSDAE)-based semi-supervised generative adversarial network (GAN), termed as RGAN, is used for ETD. The combined methodology is named as GC-RGAN. In the methodology, RSDAE acts as both feature extraction technique and generator sub-model of the proposed RGAN. The proposed methodology utilizes the advantages of clustering, adversarial learning and semi-supervised EC data. Besides, to validate the effectiveness of the proposed solution, extensive simulations are performed using smart meter data. Simulation results validate the excellent ETD performance of the proposed GC-RGAN against existing ETD schemes, such as random forest and semi-supervised support vector machine. In comparison, GC-RGAN covers the ETD score of 98

Electricity Theft (ET) causes monetary losses for power utilities in the energy sector. It occurs when electricity is consumed without being billed. Several methods are available for automatically detecting ET. Most of these methods evaluate Electricity Consumption (EC) records. However, these methods either have a low Detection Rate (DR) or a high deployment cost with a high False Positive Rate (FPR). Moreover, it is difficult to identify fraudulent consumers based solely on EC records. In addition, owing to data imbalances, such methods prove to be inefficient for classification. To solve the aforementioned problems, we have proposed a combination of various techniques. The first one is the Fastfood Transform, which is used for dimensionality reduction, along with the Time Series Lag Embedded Network (TLENET) neural network, used for classification between honest and dishonest consumers. The second one is the Wavelet Transform used for dimensionality reduction with TLENET, and the third one is the Nyström method used for dimensionality reduction with TLENET. To tackle the risk of high variance that results in overfitting in Deep Learning (DL) models, a Localized Random Affine Shadowsampling (LoRAS) data balancing technique is used. We have employed various data balancing techniques to analyze the performance of our system model. A game theory based approach, SHapley Additive exPlanations (SHAP), is implemented for explaining the output of our deep model. We have used a real-world dataset, referred as the State Grid Corporation of China (SGCC), to perform the simulations. Our model has achieved 94% accuracy, 92% F1-score, 93% Area Under Curve-Receiver Operating Characteristics (AUC-ROC), and 87% Matthews Correlation Coefficient (MCC) with LoRAS, Wavelet Transform, and TLENET. With dimensionality reduction using Fastfood Transform, our model has achieved 93% accuracy, 92% F1-score, 92% AUC-ROC, and 85% MCC. When the Nyström method is employed for dimensionality reduction, our model has achieved 94% accuracy, 92% F1-score, 90% AUC-ROC, and MCC 85%. Extensive experiments indicate that the proposed model outperforms the existing conventional detectors.

The Middle East region is a strategic driver of the global economy. However, ensuring environmental sustainability in the context of rapid urban and economic changes remains a major challenge for most Middle Eastern countries. Although researchers have widely examined factors affecting carbon dioxide emissions (CO _2 ), little attention has been paid to the Middle Eastern countries. This study uses an ARDL model to examine the nexus between urbanization, energy consumption, economic growth, and CO _2 emissions for three Middle Eastern countries, (Saudi Arabia, Egypt and Jordan) based on panel data for the period from 1990 to 2023. Findings reveal that urbanization has had no significant impact on CO _2 emissions in Egypt; this is not in line with the findings for Saudi Arabia and Jordan, where urbanization has reduced CO _2 emissions in the long- and short-run, indicating that urban policies are well matched with environmental goals in both countries. However, empirical results indicate that energy consumption has had a positive effect on CO _2 emissions in the long- and short-run in all three countries and economic growth has also had a positive impact on CO _2 emissions. The fact that economic growth has been unable to mitigate CO _2 emissions indicates a mismatch between economic policies and environmental goals. This article suggests a series of valuable insights for policymakers to reduce CO _2 emissions.

Accurate predictions of stock markets are important for investors and other stakeholders of the equity markets to formulate profitable investment strategies. The improved accuracy of a prediction model even with a slight margin can translate into considerable monetary returns. However, the stock markets' prediction is regarded as an intricate research problem for the noise, complexity and volatility of the stocks' data. In recent years, the deep learning models have been successful in providing robust forecasts for sequential data. We propose a novel deep learning-based hybrid classification model by combining peephole LSTM with temporal attention layer (TAL) to accurately predict the direction of stock markets. The daily data of four world indices including those of U.S., U.K., China and India, from 2005 to 2022, are examined. We present a comprehensive evaluation with preliminary data analysis, feature extraction and hyperparameters' optimization for the problem of stock market prediction. TAL is introduced post peephole LSTM to select the relevant information with respect to time and enhance the performance of the proposed model. The prediction performance of the proposed model is compared with that of the benchmark models CNN, LSTM, SVM and RF using evaluation metrics of accuracy, precision, recall, F1-score, AUC-ROC, PR-AUC and MCC. The experimental results show the superior performance of our proposed model achieving better scores than the benchmark models for most evaluation metrics and for all datasets. The accuracy of the proposed model is 96% and 88% for U.K. and Chinese stock markets respectively and it is 85% for both U.S. and Indian markets. Hence, the stock markets of U.K. and China are found to be more predictable than those of U.S. and India. Significant findings of our work include that the attention layer enables peephole LSTM to better identify the long-term dependencies and temporal patterns in the stock markets' data. Profitable and timely trading strategies can be formulated based on our proposed prediction model.

This study aims to assess the progress towards Sustainable Development Goal 11 (SDG 11) in Al-Madinah Al-Munawwarah, Saudi Arabia. The study also examines challenges that encounter SDG 11. To this end, six targets consisting of 40 indicators of SDG 11 have been adopted, relying on literature, international and national technical reports, and the personal perspectives of twelve experts. Overall progress towards SDG 11 has shown significant improvement considerably, with a middle level, particularly after Saudi Vision 2030 was approved. The findings show that three targets including affordable housing, sustainable transportation, and sustainable urbanization, have achieved average progress, and two targets, including air quality, and waste management, and access to green areas have been demonstrated low progress, as well as a single target, which is the preservation and protection of natural and cultural heritage, which has achieved optimal progress. The results highlight several challenges that hinder progress towards SDG 11, but the level of these challenges varies from one target to another target, ranging from moderate to major challenges. These challenges should be considered in continuing urban strategies and could be reduced by establishing resource-saving and innovative community urban renewal programs.

The global economy has reported an unprecedented increase in growth rates over the last 2 decades, due to rapid evolution in transportation and communications. The rapid growth of international trade has increased the demand for fossil fuel, leading to exacerbated environmental risks. Air transportation is an essential operational practice in trade openness and has many economic benefits. However, its effect on CO2 emissions is not well understood. Studies on the causal relationships between air transportation, trade openness, economic growth, and CO2 emissions are lacking, especially across Middle Eastern countries. This study targets Saudi Arabia, one of the largest countries in the Middle East region in terms of economic capabilities and geographical area, to investigate the impact of air transportation, trade openness, and economic growth on CO2 emissions. To this end, data was derived from the World Development Indicators (WDI) established by the World Bank for the period 1991–2023. An autoregressive, distributed lag autoregressive distributed lag (ARDL) model was used to analyze associations among the study variables; the empirical findings confirm that air transportation, trade openness, and economic growth have positive and statistically significant effects on CO2 emissions in both long- and short-run scenarios. However, the results illustrate that economic growth alone is unable to sufficiently reduce CO2 emissions in Saudi Arabia, indicating a lack of connection between economic policies and environmental goals. Thus, these results indicate that the Environmental Kuznets Curve (EKC) hypothesis is not valid for Saudi Arabia. In addition, this study provides useful insights for policymakers to mitigate CO2 emissions. Suggestions include attracting foreign investment, modifying the structure of trade, mitigating the reliance on imports and enhancing exports, while focusing on green strategies for economic growth, replacing fossil fuels with clean and renewable sources, subsidizing environmentally friendly technologies, and enacting decarbonizing regulations.

Recently, the potential for reclaimed water for irrigation purposes has been highlighted. Nonetheless, reclaimed water quality can vary over time and the sources; therefore, their control before using it becomes necessary. In this article, we propose a wireless sensor network for monitoring reclaimed water quality in the water grid to evaluate the suitability of water for green areas irrigation. The system comprises sensor nodes and actuator nodes communicated with long range (LoRa) technology. This article focused on the development of the sensors based on two coils and evaluated the effect of the capacitor included in the conditioning circuit on the sensor signal. The novelty of the proposed sensor is that the coils are isolated from the water, and only a portion of enameled copper wire is in direct contact with the water. A total of 14 capacitors were used to select the most appropriate configuration to maximize the signal differences when the water’s salinity changes. Four configurations are analyzed in detail, including a calibration and verification stage. To determine the best configuration, six parameters, including the working frequency, the maximum output voltage, or the average absolute and relative errors, among others, are considered. With an average absolute error of 1.09 mg/L and a working frequency of 812 kHz, configuration 3 has been selected.

Bitcoin has a reputation of being used for unlawful activities, such as money laundering, dark web transactions, and payments for ransomware in the context of smart cities. Blockchain technology prevents illegal transactions, but cannot detect these transactions. Anomaly detection is a fundamental technique for recognizing potential fraud. The heuristic and signature-based approaches were the foundation of earlier detection techniques, but tragically, these methods were insufficient to explore the entire complexity of anomaly detection. Machine Learning (ML) is a promising approach to anomaly detection, as it can be trained on large datasets of known malware samples to identify patterns and features of the transactions. Researchers are focusing on determining an efficient fraud and security threat detection model that overcomes the drawbacks of the existing methods. Therefore, ensemble learning can be applied to anomaly detection in Bitcoin by combining multiple ML classifiers. In the proposed model, the ADASYN-TL (Adaptive Synthetic + Tomek Link) balancing technique is used for data balancing. Random search, grid search and Bayesian optimization are used for hyperparameter tuning. The hyperparameters have a great impact on the performance of the model. For classification, we used the stacking model by combining Decision Tree, Naive Bayes, K-Nearest Neighbors, and Random Forest. We used SHapley Additive exPlanation (SHAP) to interpret the predictions of the stacking model. The model also explores the performance of different classifiers using accuracy, F1-score, Area Under Curve-Receiver Operating Characteristic (AUC-ROC), precision, recall, False Positive Rate (FPR) and execution time, and ultimately selects the ideal model. The proposed model contributes to the development of effective fraud detection models that address the limitations of the existing algorithms. Our stacking model, which combines the prediction of multiple classifiers, achieved the highest F1-score of 97%, precision of 96%, recall of 98%, accuracy of 97%, AUC-ROC of 99% and FPR of 3%.

A large number of sensors are deployed for performing various tasks in the smart cities. The sensors are connected with each other through the Internet that leads to the emergence of Internet of Things (IoT). As the time passes, the number of deployed sensors is exponentially increasing. Not only this, the enhancement of sensors has also laid the base of automation. However, the increased number of sensors make the IoT networks more complex and scaled. Due to the increasing size and complexity, IoT networks of scale-free nature are found highly prone to attacks. In order to maintain the functionality of crucial applications, it is mandatory to increase the robustness of IoT networks. Additionally, it has been found that scale-free networks are resistant to random attacks. However, they are highly vulnerable to intentional, malicious, deliberate, targeted and cyber attacks where nodes are destroyed based on preference. Moreover, sensors of IoT network have limited communication, processing and energy resources. Hence, they cannot bear the load of computationally extensive robustness algorithms. A communication model is proposed in this paper to save the sensors from computational overhead of robustness algorithms by migrating the computational load to back-end high power processing clusters. Elephant Herding Robustness Evolution (EHRE) algorithm is proposed based on an enhanced communication model. In the proposed work, 6 phases of operations are used: initialization, sorting, clan updating, clan separating,selection and formation, and filtration. These process collectively increase the robustness of the scale-free IoT networks. EHRE is compared with well-known previous algorithms and is proven to be robust with a remarkable lead in performance. Moreover, EHRE is capable to achieve global optimum results in less number of iterations. EHRE achieves 95% efficiency after 60 iterations and 99% efficiency after 70 iterations. Moreover, EHRE performs 58.77% better than Enhanced Differential Evolution (EDE) algorithm, 65.22% better than Genetic Algorithm (GA), 86.35% better than Simulating Annealing (SA) and 94.77% better than Hill climbing Algorithm (HA).

AbstractEnergy management and efficient asset utilization play an important role in the economic development of a country. The electricity produced at the power station faces two types of losses from the generation point to the end user. These losses are technical losses (TL) and non‐technical losses (NTL). TLs occurs due to the use of inefficient equipment. While NTLs occur due to the anomalous consumption of electricity by the customers, which happens in many ways; energy theft being one of them. Energy theft majorly happens to cut down on the electricity bills. These losses in the smart grid (SG) are the main issue in maintaining grid stability and cause revenue loss to the utility. The automatic metering infrastructure (AMI) system has reduced grid instability but it has opened up new ways for NTLs in the form of different cyber‐physical theft attacks (CPTA). Machine learning (ML) techniques can be used to detect and minimize CPTA. However, they have certain limitations and cannot capture the energy consumption patterns (ECPs) of all the users, which decreases the performance of ML techniques in detecting malicious users. In this paper, we propose a novel ML‐based stacked generalization method for the cyber‐physical theft issue in the smart grid. The original data obtained from the grid is preprocessed to improve model training and processing. This includes NaN‐imputation, normalization, outliers' capping, support vector machine‐synthetic minority oversampling technique (SVM‐SMOTE) balancing, and principal component analysis (PCA) based data reduction techniques. The pre‐processed dataset is provided to the ML models light gradient boosting (LGB), extra trees (ET), extreme gradient boosting (XGBoost), and random forest (RF), to accurately capture all consumers' overall ECP. The predictions from these base models are fed to a meta‐classifier multi‐layer perceptron (MLP). The MLP combines the learning capability of all the base models and gives an improved final prediction. The proposed structure is implemented and verified on the publicly available real‐time large dataset of the State Grid Corporation of China (SGCC). The proposed model outperformed the individual base classifiers and the existing research in terms of CPTA detection with false positive rate (FPR), false negative rate (FNR), F1‐score, and accuracy values of 0.72%, 2.05%, 97.6%, and 97.69%, respectively.

This paper examines factors affecting community participation in the urban planning process in Riyadh, Saudi Arabia. A quantitative method employing a questionnaire style survey was used to test the research hypotheses, with a qualitative method used to obtain a better understanding of the quantitative findings. Findings reveal that the level of community participation in the urban planning process is low. Factors contributing to lack of participation are the unwillingness of individuals to participate, as well as the inability of social media platforms used by urban authorities to effectively interact with the local community. Therefore, this study recommends the establishment of a unified national strategy for social media platforms used by urban institutions, which will stimulate the use of government instruments such as dialogue and incentives, thereby enhancing social networks and the effectiveness of organizers. These activities will also ensure that all stakeholders who are interested in the process will be invited to participate, thus improving the overall level of community participation.

The Saudi Government has spared no effort to combat the COVID-19 outbreak since its declaration by the (WHO) as a worldwide pandemic on March 11, 2020. Many restrictive policy measures were deployed. They range from social distancing, to business shutdown, school and university attendance suspension, and lock-down, etc. The way the epidemic spreads in big cities and smaller towns within the region has not yet been examined within the Saudi context. This paper explores the differential spreading of the viral infection between the large city of Riyadh and its small towns in its vicinity. The main research question consists of unrevealing whether the pandemic follows a similar pattern of COVID-19 transmission within populations of urban settings of different sizes or not. To that end, the study used data provided by the Saudi Ministry of Health (MoH) and Google Community Mobility Reports to examine the spread of the virus and the effectiveness of the policies adopted to halt its transmission. For the purpose of comparison, the basic reproduction measure (R0) was calculated for both settings. For the efficiency of policies implemented, Google Mobility Data was used to reveal how Riyadh population mobility patterns were affected by these policies. Google Mobility Data was compared to the evolution of COVID-19 confirmed cases from March 9, 2020 to May 28, 2021.

The Saudi Government has spared no effort to combat the COVID-19 outbreak since its declaration by the (WHO) as a worldwide pandemic on March 11, 2020. Many restrictive policy measures were deployed. They range from social distancing, to business shutdown, school and university attendance suspension, and lock-down, etc. The way the epidemic spreads in big cities and smaller towns within the region has not yet been examined within the Saudi context. This paper explores the differential spreading of the viral infection between the large city of Riyadh and its small towns in its vicinity. The main research question consists of unrevealing whether the pandemic follows a similar pattern of COVID-19 transmission within populations of urban settings of different sizes or not. To that end, the study used data provided by the Saudi Ministry of Health (MoH) and Google Community Mobility Reports to examine the spread of the virus and the effectiveness of the policies adopted to halt its transmission. For the purpose of comparison, the basic reproduction measure (R0) was calculated for both settings. For the efficiency of policies implemented, Google Mobility Data was used to reveal how Riyadh population mobility patterns were affected by these policies. Google Mobility Data was compared to the evolution of COVID-19 confirmed cases from March 9, 2020 to May 28, 2021.

Information and communication technology is changing the manner in which urban policies are designed. Saudi Arabia bases its smart initiative on the use of information and communication technologies in six dimensions, including economy, people, environment, living, mobility, and governance to improve quality of life and sustainable environment. This study draws on four Saudi Arabian cities including Riyadh, Makkah, Jeddah, and Medina, and aims to analyze their progress in the transformation into smart cities. The six identified areas were assessed using 57 indicators based on national and international information and literature. The results show that the four cities are progressing successfully into smart cities, with the highest progress evident for smart economy and the lowest progress for smart mobility in all investigated cities. Study findings show that Riyadh has made the most progress in the six smart city dimensions, concluding that Riyadh has been efficiently executing the smart city initiative with an aim to be a unique model in the world.

Cities are big consumers of energy and big producers of pollution. In the past years, the concept of smart cities has been applied to reduce the release of pollutants and to reduce energy consumption. In this article, we present a wireless sensor network (WSN) based on solid sensor nodes to detect illicit discharges in sewerage. The solid sensor is an optical sensor that uses infrared light to determine the pollutant concentration in water. At 0°, a photoreceptor receives infrared LED (IR LED) light and allows the current passage. This provokes a reduction of the internal resistance of the photoreceptor that can be measured. First, we tested different intensities of powered LEDs and resistances in the voltage divider. Once the best combinations had been selected, we calibrated our sensor. Our result suggested that the relative error of our prototype is 3.4% in the range of 200–5000 mg/L.