A comparative time series analysis and modeling of aerosols in the contiguous United States and China

Influence of multiple spatiotemporal resolutions on the performance of urban growth simulation models

The study developed a framework to investigate the impact of multiple spatial and temporal resolutions on urban growth simulation. The research utilized the convolutional long short-term memory (ConvLSTM) model and three regular models and data from 2009 to 2017 to simulate the urban area of Liangjiang New District in 2018 and determine the optimal spatiotemporal resolution for urban expansion models. The results indicated that the ConvLSTM model has the best simulation result and the ideal temporal resolution for Liangjiang district is to include the previous two years of data, with an optimal spatial resolution of 90 m and a spatiotemporal simulation zone within a two-year time step and 100 × 100 spatial information filter. At this combination, the kappa value of the ConvLSTM is 0.87 which is about 5% higher than others. Our findings revealed that the characteristics of input data can have a significant impact on simulation results and should be carefully considered during the simulation process.

Contrasting effects of clean air actions on surface ozone concentrations in different regions over Beijing from May to September 2013-2020

Due to the nonlinear impacts of meteorology and precursors, the response of ozone (O3) trends to emission changes is very complex over different regions in megacity Beijing. Based on long-term in-situ observations at 35 air quality sites (four categories, i.e., urban, traffic, northern suburban and southern suburban sites) and satellite data, spatiotemporal variability of O3, gaseous precursors, and O3-VOCs-NOx sensitivity were explored through multiple metrics during the warm season from 2013 to 2020. Additionally, the contribution of meteorology and emissions to O3 was separated by a machine-learning-based de-weathered method. The annual averaged MDA8 O3 and O3 increased by 3.7 and 2.9 μg/m3/yr, respectively, with the highest at traffic sites and the lowest in northern suburb, and the rate of Ox (O3 + NO2) was 0.2 μg/m3/yr with the highest in southern suburb, although NO2 declined strongly and HCHO decreased slightly. However, the increment of O3 and Ox in the daytime exhibited decreasing trends to some extent. Additionally, NOx abatements weakened O3 loss through less NO titration, which drove narrowing differences in urban-suburban O3 and Ox. Due to larger decrease of NO2 in urban region and HCHO in northern suburb, the extent of VOCs-limited regime fluctuated over Beijing and northern suburb gradually shifted to transition or NOx-limited regime. Compared with the directly observed trends, the increasing rate of de-weathered O3 was lower, which was attributed to favorable meteorological conditions for O3 generation after 2017, especially in June (the most polluted month); whereas the de-weathered Ox declined except in southern suburb. Overall, clean air actions were effective in reducing the atmospheric oxidation capacity in urban and northern suburban regions, weakening local photochemical production over Beijing and suppressing O3 deterioration in northern suburb. Strengthening VOCs control and keeping NOx abatement, especially in June, will be vital to reverse O3 increase trend in Beijing.

Four decades of hydrological response to vegetation dynamics and anthropogenic factors in the Three-North Region of China and Mongolia

Afforestation has been initiated in Northeast Asia to improve ecological status. The responses of the regional hydrological cycle to vegetation restoration remain insufficiently explored. This study uses a variety of satellite-derived vegetation variables and hydrological cycle components to scan the eco-hydrological regimes in the Three-North Region of China and Mongolia during the past four decades. We observe that vegetation productivity increases mainly in North China (NC), Northeast China (NEC), Northwest China (NWC), and the north of Inner Mongolia and Mongolia. Precipitation and runoff show a decreasing trend (-0.4 mm/year and - 0.6 mm/year, respectively), yet they are less correlated to the normalized difference vegetation index and leaf area index. Along with increasing vegetation productivity, evapotranspiration increases (0.05 mm/year) obviously in NC and NEC, while root soil moisture (-0.001 m³/m³/year) and terrestrial water storage (-2.0 mm/year) decrease in NC and parts of NEC and NWC. The correlation coefficient between evapotranspiration and vegetation variables is up to 0.73. Collectively, results imply one potential adverse response of terrestrial water fluxes to increasing vegetation. Independent ecological and hydrological datasets further corroborate our work. Climatic factors (i.e., downward shortwave radiation and air temperature) and human activities (i.e., aerosol optical depth, carbon dioxide, and water withdrawal) substantially affect regional hydrological cycles. Considering the increasing vegetation productivity in the Three-North Region of China and Mongolia is likely to continue in the 21st century based on the Sixth Coupled Model Intercomparison Project (CMIP6) simulations, the terrestrial water fluxes may undergo deficit pressure. Overall, this study comprehensively investigates the vegetation and hydrology interplays, and provides a reference for protecting and improving ecological-hydrological conditions in Northeast Asia.

Driving forces of UHI changes in China's major cities from the perspective of land surface energy balance

As remarkable human-induced temperature anomalies on the land surface, variations of urban heat island (UHI) and its driving factors have been investigated in numerous studies. However, few studies discussed the spatiotemporal heterogeneity of the driving forces exerted by land surface energy fluxes, i.e., net radiation, sensible heat, latent heat and heat storage, on UHI behaviors at large scale and long term. In this study, a comprehensive application of multisource datasets and statistical methods have been implemented based on land surface energy balance theory, the spatiotemporal variations of surface UHI intensity (urban-rural temperature difference) and changes of their driving forces have been quantified. The results demonstrate the dynamics of UHI intensity in 32 major cities of China from 2003 to 2017 are generally coherent with the common perception, the overall surface UHI intensity is 4.57 K higher in summer than in winter. The spatial variations of the fluxes that alter UHI intensity can be largely attributed to the varied energy interactions between vegetated/paved surface and atmosphere and the differences of background temperature and precipitation, the contribution of latent heat to UHI changes declines nearly 40% from semiarid/arid climate at the north to subtropical humid climate at the south, while the contributions of other fluxes are stable. The temporal changes of the effect of these fluxes, however, imply more complex mechanisms. The contributions of sensible heat and latent heat to UHI intensity variations are three times and eight times larger in the warm season than in the cold season respectively, indicating the influence of seasonality of background temperature, precipitation and vegetation. The low contributions of these fluxes in the cold season also suggest the significant effect of other driving forces such as anthropogenic heat, especially in semiarid/semihumid climate zones. This study highlights the temporal shifts of major driving forces of UHI intensity, the mitigation tactics for UHI in different cities and seasons should be customized for better validity.

Three Decades of Gross Primary Production (GPP) in China: Variations, Trends, Attributions, and Prediction Inferred from Multiple Datasets and Time Series Modeling

The accurate estimation of gross primary production (GPP) is crucial to understanding plant carbon sequestration and grasping the quality of the ecological environment. Nevertheless, due to the inconsistencies of current GPP products, the variations, trends and short-term predictions of GPP have not been sufficiently well studied. In this study, we explore the spatiotemporal variability and trends of GPP and its associated climatic and anthropogenic factors in China from 1982 to 2015, mainly based on the optimum light use efficiency (LUEopt) product. We also employ an autoregressive integrated moving average (ARIMA) model to forecast the monthly GPP for a one-year lead time. The results show that GPP experienced an upward trend of 2.268 g C/m2 per year during the studied period, that is, an increasing rate of 3.9% per decade since 1982. However, these trend changes revealed distinct heterogeneity across space and time. The positive trends were mainly distributed in the Yellow River and Huaihe River out of the nine major river basins in China. We found that the dynamics of GPP were concurrently affected by climate factors and human activities. While air temperature and leaf area index (LAI) played dominant roles at a national level, the effects of precipitation, downward shortwave radiation (SRAD), carbon dioxide (CO2) and aerosol optical depth (AOD) exhibited discrepancies in terms of degree and scope. The ARIMA model achieved satisfactory prediction performance in most areas, though the accuracy was influenced by both data values and data quality. The model can potentially be generalized for other biophysical parameters with distinct seasonality. Our findings are further verified and corroborated by four widely used GPP products, demonstrating a good consistency of GPP trends and prediction. Our analysis provides a robust framework for characterizing long-term GPP dynamics that shed light on the improved assessment of the environmental quality of terrestrial ecosystems.

Impact of the COVID-19 lockdowns on electricity and natural gas consumption in the different industrial zones and forecasting consumption amounts: Turkey case study

The COVID-19 lockdowns have adversely affected the national economies and caused fluctuations in the energy industry. This study examined how the lockdowns during the COVID-19 pandemic affected the amount of electricity and natural gas consumption in four organized industrial zones in the Turkey. A significant decrease was observed in electricity and natural gas consumption amounts in April and May when lockdowns were also applied in four industrial zones. In April, electricity consumption decreased between 72 and 43%, and natural gas consumption decreased between 77 and 57%. In May, electricity consumption decreased between 60 and 32%, and natural gas consumption decreased between 69 and 45%. These decreases in industrial zones show that the economy has been significantly affected. Furthermore, in this study, Auto-Regressive Integrated Moving Average (ARIMA) and Holt-Winters models were developed to predict electricity and natural gas consumption of an industrial zone. ARIMA(0,0,2)(2,1,0)₇ and ARIMA(0,0,2)(0,1,1)₇ models were chosen as the best model for the electricity and natural gas consumption data respectively with a minimum MAPE_Electricity was 1.37%, RMSE_Electricity was 87.2, R²_Electricity was 0.99, MAPE_Gas was 5.42% and RMSE_Gas was 50.9, R²_Gas was 0.92. Electricity and natural gas consumption was forecasted for the next ten days (10–19 March 2021) according to ARIMA models with 80% and 95% confidence intervals. In addition, in this study, the impact of low energy usage in the industrial zone due to the COVID-19 lockdowns on model prediction performance was also examined. The obtained results showed that the COVID-19 lockdowns were reduced the ARIMA model prediction accuracy.

Estimation of COVID-19 under-ascertainment in Kano, Nigeria during the early phase of the epidemics

This study aims to estimate the number of COVID-19 cases under- ascertained (η), and the basic reproduction number (R0) during the early stage of epidemic in Kano, Nigeria. We adopt a simple exponential growth model to capture the patterns of COVID-19 early epidemic curve in Kano. The R₀ is estimated at 2.7 (95%CI: 2.5, 3.0). We find that the number of COVID-19 cases under-ascertained likely occurred during the fourth week of April 2020, and should be considered for future epidemiological investigations and mitigation plan.

Monitoring and prediction of dust concentration in an open-pit mine using a deep-learning algorithm

PURPOSE

Dust pollution is currently one of the most serious environmental problems faced by open-pit mines. Compared with underground mining, open-pit mining has many dust sources, and a wide area of influence and complicated changes in meteorological conditions can result in great variations in dust concentration. Therefore, the prediction of dust concentrations in open-pit mines requires research and is of great significance for reducing environmental pollution and personal health hazards.

METHODS

This study is based on monitoring of the concentration of total suspended particulate (TSP) in the Anjialing open-pit coal mine in Pingshuo. This paper proposes a hybrid model based on a long short-term memory (LSTM) network and the attention mechanism (LSTM-Attention) and applies it to the prediction of TSP concentration. The LSTM model reflects the historical process of an input time series, and the attention mechanism extracts the inherent characteristics of the input parameters to assign weights based on the importance of the influencing factors. The autoregressive integrated moving average (ARIMA) and LSTM models are also used to predict the TSP concentration. Finally, several statistical measures of error are used to evaluate the accuracy of the model and perform a sensitivity analysis.

RESULTS

It was found that, in general, the TSP concentration was highest in the period 08:00-09:00 and lowest in the period 15:00-16:00. In addition to the influence of meteorological parameters and normal operations, the reason for this trend is the presence of an inversion layer above the open-pit mine. The results show that, compared with the ARIMA and LSTM models, the LSTM-Attention model is more stable and has a prediction accuracy that is 5.6% and 3.0% greater, respectively.

CONCLUSION

This model can be applied to the prediction of dust concentrations in open-pit mines and provide guidance on when to carry out dust-suppression work. It has expansibility and is potentially valuable for application in a wide range of areas.

COVID-19 prevalence estimation: Four most affected African countries

The world at large has been confronted with several disease outbreak which has posed and still posing a serious menace to public health globally. Recently, COVID-19 a new kind of coronavirus emerge from Wuhan city in China and was declared a pandemic by the World Health Organization. There has been a reported case of about 8622985 with global death of 457,355 as of 15.05 GMT, June 19, 2020. South-Africa, Egypt, Nigeria and Ghana are the most affected African countries with this outbreak. Thus, there is a need to monitor and predict COVID-19 prevalence in this region for effective control and management. Different statistical tools and time series model such as the linear regression model and autoregressive integrated moving average (ARIMA) models have been applied for disease prevalence/incidence prediction in different diseases outbreak. However, in this study, we adopted the ARIMA model to forecast the trend of COVID-19 prevalence in the aforementioned African countries. The datasets examined in this analysis spanned from February 21, 2020, to June 16, 2020, and was extracted from the World Health Organization website. ARIMA models with minimum Akaike information criterion correction (AICc) and statistically significant parameters were selected as the best models. Accordingly, the ARIMA (0,2,3), ARIMA (0,1,1), ARIMA (3,1,0) and ARIMA (0,1,2) models were chosen as the best models for SA, Nigeria, and Ghana and Egypt, respectively. Forecasting was made based on the best models. It is noteworthy to claim that the ARIMA models are appropriate for predicting the prevalence of COVID-19. We noticed a form of exponential growth in the trend of this virus in Africa in the days to come. Thus, the government and health authorities should pay attention to the pattern of COVID-19 in Africa. Necessary plans and precautions should be put in place to curb this pandemic in Africa.

Validation of MODIS C6.1 and MERRA-2 AOD Using AERONET Observations: A Comparative Study over Turkey

This study validated MODIS (Moderate Resolution Imaging Spectroradiometer) of the National Aeronautics and Space Agency, USA, Aqua and Terra Collection 6.1, and MERRA-2 (Modern-ERA Retrospective Analysis for Research and Application) Version 2 of aerosol optical depth (AOD) at 550 nm against AERONET (Aerosol Robotic Network) ground-based sunphotometer observations over Turkey. AERONET AOD data were collected from three sites during the period between 2013 and 2017. Regression analysis showed that overall, seasonally and daily statistics of MODIS are better than MERRA-2 by the mean of coefficient of determination (R2), mean absolute error (MAE), and relative root mean square deviation (RMSDrel). MODIS combined Terra/Aqua AOD and MERRA-2 AOD corresponding to morning and noon hours resulted in better results than individual sub datasets. A clear annual cycle in AOD was detected by the three platforms. However, overall, MODIS and MERRA-2 tend to overestimate and underestimate AOD, respectively, in comparison with AERONET. MODIS showed higher efficiency in detecting extreme events than MERRA-2. There was no clear relation found between the accuracy in MODIS/MERRA-2 AOD and surface relative humidity (RH).

Estimation of COVID-19 prevalence in Italy, Spain, and France

At the end of December 2019, coronavirus disease 2019 (COVID-19) appeared in Wuhan city, China. As of April 15, 2020, >1.9 million COVID-19 cases were confirmed worldwide, including >120,000 deaths. There is an urgent need to monitor and predict COVID-19 prevalence to control this spread more effectively. Time series models are significant in predicting the impact of the COVID-19 outbreak and taking the necessary measures to respond to this crisis. In this study, Auto-Regressive Integrated Moving Average (ARIMA) models were developed to predict the epidemiological trend of COVID-19 prevalence of Italy, Spain, and France, the most affected countries of Europe. The prevalence data of COVID-19 from 21 February 2020 to 15 April 2020 were collected from the World Health Organization website. Several ARIMA models were formulated with different ARIMA parameters. ARIMA (0,2,1), ARIMA (1,2,0), and ARIMA (0,2,1) models with the lowest MAPE values (4.7520, 5.8486, and 5.6335) were selected as the best models for Italy, Spain, and France, respectively. This study shows that ARIMA models are suitable for predicting the prevalence of COVID-19 in the future. The results of the analysis can shed light on understanding the trends of the outbreak and give an idea of the epidemiological stage of these regions. Besides, the prediction of COVID-19 prevalence trends of Italy, Spain, and France can help take precautions and policy formulation for this epidemic in other countries.

Forecasting the Spreading of COVID-19 across Nine Countries from Europe, Asia, and the American Continents Using the ARIMA Models

Since mid-November 2019, when the first SARS-CoV-2-infected patient was officially reported, the new coronavirus has affected over 10 million people from which half a million died during this short period. There is an urgent need to monitor, predict, and restrict COVID-19 in a more efficient manner. This is why Auto-Regressive Integrated Moving Average (ARIMA) models have been developed and used to predict the epidemiological trend of COVID-19 in Ukraine, Romania, the Republic of Moldova, Serbia, Bulgaria, Hungary, USA, Brazil, and India, these last three countries being otherwise the most affected presently. To increase accuracy, the daily prevalence data of COVID-19 from 10 March 2020 to 10 July 2020 were collected from the official website of the Romanian Government GOV.RO, World Health Organization (WHO), and European Centre for Disease Prevention and Control (ECDC) websites. Several ARIMA models were formulated with different ARIMA parameters. ARIMA (1, 1, 0), ARIMA (3, 2, 2), ARIMA (3, 2, 2), ARIMA (3, 1, 1), ARIMA (1, 0, 3), ARIMA (1, 2, 0), ARIMA (1, 1, 0), ARIMA (0, 2, 1), and ARIMA (0, 2, 0) models were chosen as the best models, depending on their lowest Mean Absolute Percentage Error (MAPE) values for Ukraine, Romania, the Republic of Moldova, Serbia, Bulgaria, Hungary, USA, Brazil, and India (4.70244, 1.40016, 2.76751, 2.16733, 2.98154, 2.11239, 3.21569, 4.10596, 2.78051). This study demonstrates that ARIMA models are suitable for making predictions during the current crisis and offers an idea of the epidemiological stage of these regions.