Mapping Chini Lake (Pahang, Malaysia) using Sentinel-2 images to determine the effect of acid mine drainage in the pre- to post-COVID-19 restriction period.

Mining activities in the Chini Lake catchment area have been extensive for several years, contributing to acid mine drainage (AMD) events with high concentrations of iron (Fe) and other heavy metals impacting the surface water. However, during the restriction period due to the COVID-19 outbreak, anthropogenic activities have been suspended, which clearly shows a good opportunity for a better environment. Therefore, we aimed to analyze the variation of AMD-associated water pollution in three main zones of the Chini Lake catchment area using Sentinel-2 data for the periods pre-movement control order (MCO), during MCO, and post-MCO from 2019 to 2021. These three zones were chosen due to their proximity to mining areas: zone 1 in the northeastern part, zone 2 in the southeastern part, and zone 3 in the southern part of the Chini Lake area. The acid mine water index (AMWI) was a specific index used to estimate acid mine water. The AMWI values from Sentinel-2 images exhibited that the mean AMWI values in all zones during the MCO period decreased by 14% compared with the pre-MCO period. The spatiotemporal analysis found that the highest polluted zones were recorded in zone 1, followed by zone 3 and zone 2. As compared with during the MCO period, the maximum percentage of increment during post-MCO in all zones was up to 25%. The loosened restriction policy has resulted in more AMD flowing into surface water and increased pollution in Chini Lake. As a whole, our outputs revealed that Sentinel-2 data had a major potential for assessing the AMD-associated pollution of water.

Impact of COVID-19 Pandemic on Traffic Volume and Air Quality in Urban Areas

The large transmission of COVID-19 has resulted in a deep impact on the surrounding urban environments, especially on air quality and traffic flows. The objective of this study was to analyze air pollutant concentrations (PM10, SO2, NO2, CO, and O3) and traffic volumes at five congested districts (Bundaran HI, Kelapa Gading, Jagakarsa, Lubang Buaya, and Kebon Jeruk) within Jakarta city impacted by the large-scale social restriction (LSSR) policy. Air quality data during three periods;before, during, and after the LSSR at five observed districts was obtained from the Department of Environment of Jakarta using the Air Quality Monitoring (AQMS) tool. While vehicle speed data were obtained from the waze data during the study period. In this study, air pollutant data during three periods;before, during, and after the LSSR were compared with vehicle speed and meteorological data using a statistical analysis. Results revealed the mean traffic volume at all five districts has greatly reduced by 19% from before to during the LSSR period. It was consistent with the mean PM10, NO2, CO, and SO2 concentrations which also dropped about 46%, 45%, 30%, and 23% respectively. In contrast, the concentrations of air pollutants significantly increased after the LSSR period. During the LSSR period, the traffic volume was negatively associated with the O3 concentration (r = −.86, p < .01), it was different with before the LSSR periods where the traffic volume associated with CO (r = .88, p < .01) and NO2 (r = .89, p < .01). The broad analysis of changes in air pollutants and traffic volumes can be used by the authorities to arrange a good air quality management and an effective way for current and future scenarios.

Changes in air quality during and after large-scale social restriction periods in Jakarta city, Indonesia

COVID-19 outbreak has constrained human activities in Jakarta, Indonesia during the large-scale social restriction (LSSR) period. The objective of this study was to evaluate the changes in the spatial variation of air pollutants over Jakarta during and after the LSSR periods. This study used satellite retrievals such as OMI, AIRS, and MERRA-2 satellite data to assess spatial variations of NO2, CO, O3, SO2, and PM2.5 from May to June 2020 (during the LSSR period) and from July to August 2020 (after the LSSR period) over Jakarta. The satellite images were processed using GIS software to increase the clarity of the images. The relationship between air pollutants and meteorological data was analyzed using Pearson correlation. The results showed the levels of NO2, PM2.5, O3, and CO increased by 59.4%, 21.2%, 16.2%, and 1.0%, respectively, while SO2 decreased by 19.1% after the LSSR period. The temperature value was inversely correlated with PM2.5, NO2, and SO2 concentrations. Furthermore, the backward trajectory analysis revealed that air pollutants from outland areas such as the east and southeast carried more particulate matter and gases pollutants, which contributed to the air pollution during and after the LSSR periods. As a whole, the COVID-19 outbreak had bad impacts on human health, but the increase in air pollutants levels after loosening the LSSR policy could also lead to a higher risk of severe respiratory diseases. This study provides new insight into air pollutant distribution during and after LSSR periods and recommends an effective method of mitigating the air pollution issues in Jakarta.

Atmospheric Methane Condition over the South Sumatera Peatland during the COVID-19 Pandemic

Recent anthropogenic activities have degraded peatlands, the largest natural reservoir of soil carbon, thereby reducing their carbon uptake from the atmosphere. As one of the primary sources of methane (CH4) emissions in terrestrial ecosystems, peatlands also contribute to atmospheric greenhouse gases. During the coronavirus disease 2019 (COVID-19) pandemic, Indonesia implemented a lockdown referred to as large-scale social restrictions (LSSR) in areas with high case numbers. To evaluate the effects of anthropogenic activity on peatlands, we investigated the CH4 concentrations in the atmosphere above the tropical peatlands of the Indonesian province South Sumatra before the LSSR (March 2020), during the LSSR (May 2020), and during the corresponding months of the previous year (March and May 2019). Using satellite-retrieved data from NASA, viz., the CH4 concentration and gross primary production (GPP) measured by the Atmospheric Infrared Sounder (AIRS) on board Aqua and Moderate Resolution Imaging Spectroradiometer (MODIS) on board Terra, respectively, we discovered a decrease of approximately 5.5% in the mean CH4 concentration (which averaged 1.73 ppm across the periods prior to lockdown) as well as an increase in the GPP (which ranged from 53.3 to 63.9 g C m–2 day–1 during the lockdown, indicating high atmospheric carbon intake) during the LSSR. Thus, the restrictions during lockdown, which reduced anthropogenic activities, such as land use conversion and biomass burning, and related events, such as peatland and forest fires, significantly influenced the level of atmospheric CH4 above the peatlands in Indonesia.

New COVID-19 variant (B.1.1.7): Forecasting the occasion of virus and the related meteorological factors.

BACKGROUND: World Health Organization has reported fifty countries have now detected the new coronavirus (B.1.1.7 variant) since a couple of months ago. In Indonesia, the B.1.1.7 cases have been found in several provinces since January 2021, although they are still in a lower number than the old variant of COVID-19. Therefore, this study aims to create a forecast analysis regarding the occasions of COVID-19 and B.1.1.7 cases based on data from the 1st January to 18th March 2021, and also analyze the association between meteorological factors with B.1.1.7 incidences in three different provinces of Indonesia such as the West Java, South Sumatra and East Kalimantan. METHODS: We used the Autoregressive Moving Average Models (ARIMA) to forecast the number of cases in the upcoming 14 days and the Spearman correlation analysis to analyze the relationship between B.1.1.7 cases and meteorological variables such as temperature, humidity, rainfall, sunshine, and wind speed. RESULTS: The results of the study showed the fitted ARIMA models forecasted there was an increase in the daily cases in three provinces. The total cases in three provinces would increase by 36% (West Java), 13.5% (South Sumatra), and 30% (East Kalimantan) as compared with actual cases until the end of 14 days later. The temperature, rainfall and sunshine factors were the main contributors for B.1.1.7 cases with each correlation coefficients; r = -0.230; p < 0.05, r = 0.211; p < 0.05 and r = -0.418; p < 0.01, respectively. CONCLUSIONS: We recapitulated that this investigation was the first preliminary study to analyze a short-term forecast regarding COVID-19 and B.1.1.7 cases as well as to determine the associated meteorological factors that become primary contributors to the virus spread.

Spatial distribution of COVID-19 cases, epidemic spread rate, spatial pattern, and its correlation with meteorological factors during the first to the second waves.

Currently, many countries all over the world are facing the second wave of COVID-19. Therefore, this study aims to analyze the spatial distribution of COVID-19 cases, epidemic spread rate, spatial pattern during the first to the second waves in the South Sumatra Province of Indonesia. This study used the geographical information system (GIS) software to map the spatial distribution of COVID-19 cases and epidemic spread rate. The spatial autocorrelation of the COVID-19 cases was carried out using Moran's I, while the Pearson correlation was used to examining the relationship between meteorological factors and the epidemic spread rate. Most infected areas and the direction of virus spread were predicted using wind rose analysis. The results revealed that the epidemic rapidly spread from August 1 to December 1, 2020. The highest epidemic spread rate was observed in the Palembang district and in its peripheral areas (dense urban areas), while the lowest spread rate was found in the eastern and southern parts of South Sumatra Province (remote areas). The spatial correlation characteristic of the epidemic distribution exhibited a negative correlation and random distribution. Air temperature, wind speed, and precipitation have contributed to a significant impact on the high epidemic spread rate in the second wave. In summary, this study offers new insight for arranging control and prevention strategies against the potential of second wave strike.

The relationship between air pollutants and COVID-19 cases and its implications for air quality in Jakarta, Indonesia

World Health Organization (WHO) has announced that COVID-19 as a global pandemic and public health emergency. Previous studies have revealed that COVID-19 was an infectious disease and it could remain viable in ambient air for hours. Therefore, this study aims to examine the correlation between air pollutants (PM2.5, PM10, CO, SO2, NO2 and O3) and COVID-19 spread in Jakarta, Indonesia. Furthermore, this study also evaluates the impact of large-scale social restriction (LSSR) on air pollution index (API). Result of study found that air pollution index of PM2.5, PM10, CO, SO2 and NO2 decreased by 9.48%, 15.74%, 29.17%, 6.26% and 18.34% during LSSR period. While, for O3 showed an increase by 4.06%. Another result also found significantly positive correlations of SO2, CO and PM2.5 with COVID-19 cases. An exposure to SO2, CO and PM2.5 has driven the area become vulnerable for COVID-19 infection. Our findings indicated that the relationship between air pollutants and COVID-19 spread could provide a new notion for precaution and control method of COVID-19 outbreak.

Impact of the wind conditions on COVID-19 pandemic: A new insight for direction of the spread of the virus.

COVID-19 pandemic is the global health crisis of our time. A recent study has found that the virus can remain viable in air for multiple hours, thus the spread of virus can be affected by wind conditions such as wind speed and direction. Therefore, this study aims to analyze the impact of wind conditions on COVID-19 pandemic in Jakarta, Indonesia. The wind parameters were evaluated using wind roses analysis to estimate the direction of spread of virus. The effect of meteorological factors such as wind speed, temperature, sunshine hours, rainfall and humidity on COVID-19 cases was examined using Spearman correlation test. Result of study reveals that a low wind speed is significantly correlated with a higher COVID-19 cases (r = -0.314; p < 0.05). Similarly, low temperatures and sunshine hours are correlated with a higher COVID-19 cases (r = -0.447; p < 0.01, r = -0.362; p < 0.05, respectively). However, there are not significant linear correlations between humidity and rainfall with COVID-19 cases (p > 0.05). In addition, wind rose diagrams indicate that the highest COVID-19 cases fits in with wind direction blows. In study area, the dominant wind direction blows to the Southeast and East parts of the area with wind speed value is low in range from 3.60 to 5.70 m/s. In conclusion, low wind speed is a contributor to increase COVID-19 cases.