ABSTRACT
Resilience in business continuity of an entire industrial complex has direct local socioeconomic impact;however, there are few methods available for objective assessment of its status. This study investigated whether change in air quality could explain the state of economic activity in an industrial complex. Concentrations of PM2.5 and NO2 above several industrial complexes in central Thailand were extracted using the Google Earth Engine™ and analyzed to examine their temporal characteristics in relation to decline in business activity caused by the COVID-19 pandemic. Results confirmed that industrial complexes whose activities were diminished by the pandemic showed concurrent trends of reduction in each pollutant, proving that the concentration of airborne substances has potential to reveal the level of activity of industrial complexes. To enhance the application potential of the proposed method, further study should investigate specific causal inferences by extracting the characteristics of other airborne substances, and consider industrial complexes that include a greater number of companies and major industries. © 2023 IEEE.
ABSTRACT
Human health is severely endangered by the novel coronavirus (COVID-19). It is viewed as the worst global health threat humans have faced since the second world war and the WHO recognized it as a pandemic on March 11, 2020. This pandemic led several nations to adopt statewide lockdowns, while the industrial, construction, and transportation activities in several nations were disrupted, which lead to a significant shift in air pollutants. The lockdown, however, significantly impacted the environment and air quality in distinct cities. There are numerous ground stations deployed by pollution control organizations to monitor and collect the air pollutants data, but it is not feasible to set up a ground station in every city. In places where ground stations are not available for data collection, Google Earth Engine (GEE) satellite captured data can be used for data analysis. This study aimed to analyze the changes in air pollutants during the different lockdowns in India, such as nitrogen dioxide(NO2), sulfur dioxide(SO2), and carbon monoxide(CO) that contribute significantly to air pollution. In India, lockdowns were imposed during different periods of 2020, 2021, and 2022, according to COVID-19 waves. The air pollutants data during different waves have been analyzed and compared with the pre-COVID year (2019) data for the same duration. According to the study results, N O2 and S O2 were drastically reduced, but only a minor reduction in CO. Delhi, Jaipur, Ahmedabad, and Mumbai were among the major cities that saw the largest reduction, which was up to 60%. © 2022 IEEE.
ABSTRACT
The Malaysian government has implemented extensive physical distancing measures to prevent and control virus transmission in response to the pandemic COVID-19. Particularly in the Kuala Lumpur, Putrajaya, and Selangor regions, quantitative, spatially disaggregated information about the population-scale shifts in an activity caused by these measures is extremely rare. A next-generation space-borne low-light imager called the Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS-DNB) can monitor changes in human activities. However, a cross-country examination of COVID-19 replies has not yet utilized the potential. To understand how communities have complied with COVID-19 measures in the two years since the pandemic. This study aims to quantify nighttime light (NTL) before and during COVID-19 using multi-year (2019-2021) monthly time series data derived from VIIRS nighttime light (NTL) products covering urban areas in Selangor, Putrajaya, and Kuala Lumpur. The NTL was processed in the Google Earth Engine (GEE) platform. NTL data has documented the link between curfew orders, nationwide closures, and the uneven response to control measures between and within the areas. Our findings demonstrate satellite images from VIIRS DNB can examine public opinion regarding national curfews and lockdowns, laws, and the sociocultural elements that influence their effectiveness, particularly in unstable and sparsely populated areas. Statistical T-test analysis revealed that the p-value for Kuala Lumpur was 0.01687, and less than 0.05 meant a significant difference between NTL reduction before and during COVID-19. Petaling showed a p-value of 0.0034 and less than 0.05, indicating a significant difference between NTL reduction before and during COVID-19. However, for area Putrajaya, the p-value is 0.0957, and more than 0.05 means there is no significant difference between the reduction of NTL before and during COVID-19. © 2022 IEEE.
ABSTRACT
Environmental education allows students to investigate environmental problems, formulate necessary tests and analyses, and undertake activities to improve the condition of the environment, which are primarily done through field trips, forest visits, and community immersions. However, these outdoor activities have been discontinued and prohibited due to the travel restrictions brought by the COVID-19 pandemic. This study utilized virtual Google Earth Learning Activities (GELA) to provide alternative virtual experiences for the students learning environmental concepts. Moreover, this study aimed to assess the effects of GELA on students' environmental awareness and environmental attitudes. A quasi-experimental research design following a mixed-method approach was used in the study. The participants were 156 senior high school students from a private school in Manila, Philippines. Survey questionnaires were distributed online via Google forms, and the Focus Group Discussion was conducted synchronously thru Zoom video conferencing. Results revealed that students' environmental awareness moderately increased after performing the GELA (g=0.87, p<0.05), while there was a significantly high increase in students' environmental attitudes (g=0.66, p<0.05). Furthermore, it was also revealed that ecological awareness could predict ecological attitude, implying that there is also a good attitude towards the environment to those with great environmental awareness. Three themes were generated in the thematic analysis: Scientific Learning, Independent Learning, and Student Engagement. Overall, this study found that GELA is an effective teaching tool enhancing students' environmental awareness and attitude. © 2022 ACM.
ABSTRACT
Gerbangkertosusila (Gresik-Bangkalan-Mojokerto-Surabaya-Sidoarjo-Lamongan) is one of the biggest metropolitan areas in Indonesia impacted hardest by COVID-19 after social restriction. High temperature conditions are an issue in the Gerbangkertosusila area. Reduced mobility and industrial activity lead to decrease in surface temperature. The research was carried out using the Statistical Mono Windows (SMW) algorithm in separate periods of time (July 2019, July 2020, October 2020, May 2021) to represent the changes between social restriction policy and the weather. This research goal is to examine the relationship between land surface temperature with changes of spectral indices, such as NDVI (Normalized Difference Vegetation Index) and NDBI (Normalized Difference Built-up Index) data. These three parameters are correlated with a simple linear regression equation to calculate how much influence occurs in each different period, then the qualitative analysis is carried out to explain the variations between the distribution of hotspot and annual temperature chart to the real conditions. The result shows strong positive correlation coefficient between changes of NDBI pixel and the LST in each period of time such as 0.62;0.80;0.70;and 0.80. Meanwhile the NDVI-LST correlation coefficient shows negative results such as-0.57;-0.43;-0.38;-0.41. This research also concludes that in the social restriction period, the Land Surface Temperature doesn't affect the variability of NDVI © 2021 SPIE.
ABSTRACT
The term of Mudik (in Bahasa) is often interpreted as the return of migrants from foreign areas to their hometowns, or largely known as massive mobility among regions that especially carried out annually during the Eid al-Fitr holiday in Indonesia. However, due to the COVID-19 pandemic occurred in 2020 and 2021, restrictions were placed on mudik during the Eid holiday. This study was conducted to see the extent of the effectiveness of the mudik restrictions carried out by the Indonesian government. This study was conducted by reviewing the levels of NO2 and CO in the months before and during the Eid al-Fitr holiday through spatiotemporal processing of images retrieved from Google Earth Engine. The data used is Sentinel-5p images to map air pollution levels from NO2 values and CO values in January-June 2019-2021. The study area includes two districts in DKI Jakarta Province and two districts in Central Java Province. The statistical tests are useful to see the trend of data that is obtained from the zonal analysis process. The statistical tests were carried out using the Mann-Kendall Test method to detect trends and the results were equipped with Sen's Slope analysis to measure the magnitude of the changes that occurred. According to the trend of NO2 and CO values obtained, the values in 2019 are higher than in 2021, and the values in 2021 are higher than in 2020. Thus, the policy of mudik restrictions in 2020 is assumed more effectively than in 2021. The trend in the levels of NO2 and CO in the air is more significant a month before the Eid al-Fitr holiday than in the Eid al-Fitr holiday. It can illustrate that the production of NO2 and CO from motor vehicles continues to increase before there are restrictions. © 2021 SPIE.
ABSTRACT
First responders and other forward deployed essential workers can benefit from advanced analytics. Limited network access and software security requirements prevent the usage of standard cloud based microservice analytic platforms that are typically used in industry. One solution is to precompute a wide range of analytics as files that can be used with standard preinstalled software that does not require network access or additional software and can run on a wide range of legacy hardware. In response to the COVID-19 pandemic, this approach was tested for providing geo-spatial census data to allow quick analysis of demographic data for better responding to emergencies. These data were processed using the MIT SuperCloud to create several thousand Google Earth and Microsoft Excel files representative of many advanced analytics. The fast mapping of census data using Google Earth and Microsoft Excel has the potential to give emergency responders a powerful tool to improve emergency preparedness. Our approach displays relevant census data (total population, population under 15, population over 65, median age) per census block, sorted by county, through a Microsoft Excel spreadsheet (xlsx file) and Google Earth map (kml file). The spreadsheet interface includes features that allow users to convert between different longitude and latitude coordinate units. For the Google Earth files, a variety of absolute and relative colors maps of population density have been explored to provide an intuitive and meaningful interface. Using several hundred cores on the MIT SuperCloud, new analytics can be generated in a few minutes. © 2021 IEEE.
ABSTRACT
Manual vehicle counting is often tedious, expensive, and time-consuming. While automatic counting from CCTV allows for annual average daily traffic estimation, CCTV files in the Philippines are not available to the public and do not fully cover all road extents. In this study, Remote Sensing and Geographic Information Systems (GIS) techniques are employed to use readily available satellite images to obtain vehicle count in selected road segments in the Central Business Districts of Quezon City before and after the COVID-19 lockdown. Using the existing Google Earth Images, a segmentation algorithm using ENVI Feature Classification was developed to allow remote counting of vehicles from the earliest image in 2018. The devised algorithm was able to delineate, identify, and classify according to the types of vehicles that are visible on the image. An average error rate of 12.24% was found by comparison of automated counts and manual counts on the images, while a regression analysis yielded a value of R2 Combining double low line 0.9227 that denoted a strong relationship between automated and manual counts. Vehicle density was calculated, and percent differences were obtained to determine the relative differences of the vehicle counts from the vehicle count of the earliest image taken in 2018. It was found that the vehicle density declined by at least 81% by March 25, 2020. The methodological framework presented in this study provides estimates of vehicle counts and vehicle density. It can be further improved if vehicle counts, on the same location and period, from field validation surveys are available. © International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives