Investigating Waste and Land Use Changes Effects on Air Pollution Using Google Earth Engine in Jakarta Metropolitan in Indonesia

Investigating Waste and Land Use Changes Effects on Air Pollution Using Google Earth Engine in Jakarta Metropolitan in Indonesia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Investigating Waste and Land Use Changes Effects on Air Pollution Using Google Earth Engine in Jakarta Metropolitan in Indonesia OSCAR UMWANZISIWEMUREMYI, ZAENAL ABIDIN, YUDI SETIAWAN This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4932823/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In recent years, air pollution has intensified due to a continuous rise in atmospheric pollutants. Emissions from anthropogenic activities have exacerbated this problem, contributing to health, environmental, and climate issues. Researchers have identified energy production, industrial processes, wildfires, and volcanic eruption as leading contributors, with waste, changes in land use and land cover (LULC) being secondary factors. Consequently, the growing global waste production and alterations in LULC are among factors in increasing pollution levels, which we seek to emphasise in this research. We specifically investigated the effects of waste and LULC on carbon monoxide (CO), Sulphur dioxide SO 2 , nitrous dioxide (NO 2 ), and formaldehyde (CH 2 O) emissions in the Jakarta Metropolitan region, Indonesia. Data were gathered from the Sentinel 2 and Sentinel-5 Precursor satellites and National Waste Management Information System Indonesia (SIPSN) from 2019 to 2023, we employed excel, image analysis, and Google Earth Engine (GEE) for data processing. ArcGIS 10.8 was used for advanced mapping. Our findings revealed that CO, SO 2 , NO 2 , and CH 2 O levels have increased the past five years in correlation with waste and LULC change, with a dip in 2020 during the COVID-19 lockdowns. Efforts to mitigate air pollution include curbing these emissions and promoting green urban planning. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences Air pollution Google Earth Engine (GEE) Land Use / Land Cover (LULC) Sentinel 2 Sentinel-5 Precursor waste Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 1. INTRODUCTION Air pollution is the release of harmful substances into the atmosphere, adversely impacting living organisms (Manisalidis et al. 2020 ). These substances may originate from natural sources such as volcanic eruptions, forest fires, sea salt, desert dust (for example in Sahara), and natural decomposition of plants and animals, as well as from anthropogenic activities including transportation, land use and land cover change (LULC), waste, industry processes, energy production, open burning, and smoking (Popescu et al. 2010 ; WHO 2021 ; US EPA 2023a ). The primary air pollutants include carbon monoxide (CO), carbon dioxide (CO 2 ), methane (CH 4 ), sulphur dioxide (SO 2 ), nitrous oxide (NO x ), particulate matter (PM), and non-methane volatile organic compounds (VOCs)(Popescu et al. 2010 ; Manisalidis et al. 2020 ). According to the world data, global emissions of CO were 443.18 million tonnes in 2019, decreasing to 427.75 million tonnes in 2020, 428.15 million tonnes in 2021, and 423.30 million tonnes in 2022. NO x emissions were 96.34 million tonnes in 2019, 89.99 million tonnes in 2020, 92.49 million tonnes in 2021, and 91.90 million tonnes in 2022. SO 2 emissions were 70.48 million tonnes in 2019, 66.19 million tonnes in 2020, 68.45 million tonnes in 2021, and 69.31 million tonnes in 2022. While non-methane VOC emissions were 133.52 million tonnes in 2019, 131.08 million tonnes in 2020, 131.35 million tonnes in 2021, and 130.79 million tonnes in 2022 (Our World In Data 2024 ). Furthermore, these pollutants are significant contributors to greenhouse gas emissions, which were approximately 29.07 billion metric tonnes of CO 2 -equivalent (CO2-eq) from natural sources in 2016 compared to 49.4 billion metric tonnes of CO 2 -eq from anthropogenic activities during the same year (YUE and GAO 2018 ; Ritchie 2020 ). Thus, these anthropogenic sources played a part in global gas emissions as follows: energy at 73.2%; agriculture, forest, and land use at 18.4%; chemical and cement industries coupled with 5.2%; and waste at 3.2% (Ritchie 2020 ). Despite a temporary reduction in gas emissions to 54.5 billion metric tonnes of CO 2 -eq in 2020 due to the COVID-19 pandemic lockdowns and economic downturn, emissions rebounded dramatically after that to 56.8 billion metric tonnes in 2021, 57.4 billion metric tonnes of CO 2 -eq, 57.4 billion metric tonnes of CO 2 -eq in 2022. The United Nations Environment Programme (UNEP) predicts emissions could reach 60 billion metric tonnes of CO 2 -eq by 2030 if the current policies persist and further exacerbate air pollution issues (UNEP 2023 ). Consequently, this air pollution is leading to 9 million human deaths annually and environmental problems such as wildlife loss, eutrophication, acid rain, global warming, and climate change (Manisalidis et al. 2020 ). However, emissions contributions vary by country, with the top 10 emitters accounting for 66.65% of global emissions (Ge et al. 2020 ), including Indonesia, for instance, 1.865 billion metric tonnes of CO 2 -eq in 2019, 1.10471 billion metric tonnes of CO 2 -eq in 2020, followed by 1.12806 billion metric tonnes of CO 2 -eq in 2021 and 1.24083 billion metric tonnes of CO 2 -eq in 2022, predominantly from CO 2 , CH 4 , N 2 O, and Fluorinated gases (F-gases), and this is projected to reach 2.868 billion metric tonnes of CO 2 -eq in 2030, as the leading pollutants, discharged from the following sources: agriculture with 15.49%, building 3.06%, fuel exploitation 21.38%, industrial combustion 14.68%, power industries: 20.44%, processes 5.8%, transport 11.74% and waste 7.72% per data of 2022 (Ministry of Environment and Forestry 2021 ; Crippa, et al. 2023). For this reason, our research aimed to shed light on the contribution of waste and LULC changes to air pollution utilizing Excel analysis for trend changes for waste, unsupervised image analysis for LULC and Google Earth Engine (GEE) to model missions of CO, NO 2 , SO 2 and CH 2 O over five years (2019–2023) in the Jakarta Metropolitan region in Indonesia. Our findings revealed significant LULC changes from 2019 to 2023, with built areas increasing while trees, flooded vegetation, and crops decreased. In addition, waste in our study area rose from 2019 to 2023 and was mismanaged. As a result, we found that the gas emissions in these regions exceeded the minimum and approached the maximum thresholds detected by the Sentinel-5 Precursor satellite. CO, NO 2 , SO 2 and CH 2 O concentrations fluctuated between 2019 and 2023. Therefore, we stressed that measures to reduce air pollution should also consider curbing waste generation and mismanagement, deforestation, and promoting sustainable land use. Moreover, we recommend reforestation, greening gardens, agroforestry, and green urban building with a focus on multi-storey buildings to mitigate air pollution in the Jakarta Metropolitan region in Indonesia. 2. METHODOLOGY 2.1. Study Area This study was conducted between November 2023 and May 2024 in Jakarta, the capital of Indonesia, and its surrounding and satellite cities and regencies, including Tangerang (Regency and City), Bogor (Regency and City), Bekasi (Regency and City) and Depok City. This region covers an area of 6800.40 Km2, situated between longitudes 106°20'0"E and 107°20'0"E, and latitudes 6°45'0"S and 5°30'0"S, as illustrated in Fig. 1 . As data of 2023, this region has a population of 32,143,225 (Badan Pusat Statistik 2024 ). These cities are primarily characterized by administration, business activities, tourism, and industries, while the regencies are predominantly focused on agriculture, tourism, and minor-scales businesses. 2.2. Data Acquisition During our study, we utilize a variety of sources and websites, detailed below, to get our data and to conduct our analyses. 2.2.1. National Waste Management Information System The National Waste Management Information System Indonesia, referred to as Sistem Informasi Pengelolaan Sampah Nasional (SIPSN) is a web-based database that has been in operation since 2018 with the purpose of storing and managing data for waste management in Indonesia. Therefore, in this study, we retrieved waste data that has reached the final disposal sites known as Tempant Tempat Pemrosesan Akhir (TPA), focusing on both cities and regencies of Tangerang, Bogor, Bekasi, the city of Depok, and the capital of Jakarta known as Daerah Khusus Ibukota Jakarta(DKI Jakarta) (SIPSN 2024 ). This data covers corrected and managed waste information from 2019 to 2023. 2.2.2. Sentinel-2 The Sentinel-2 satellite, operated by the European Space Agency (ESA), was launched on 23rd June 2015 under the Copernicus Sentinel-2 mission, and updated in 2017 and 2024 to monitoring the land and vegetation changes (ESA 2024a ). This satellite has a resolution of ten meters (10m). It categorises land and vegetation changes into nine classes: one as water, two as trees, four as flooded vegetation, five as crops, seven as built area, eight as bare ground, nine as snow, ten as clouds and eleven as rangeland (Karra et al. 2021 ). Then, we downloaded pixel data from 2018/2019 to 2023/2024 to detect LULC changes in our regions during these years. 2.2.3. Sentinel-5P The Sentinel-5P satellite, also managed by ESA, was sent into orbit on 13th October 2017 in collaboration with the Netherlands Space Office and others under the Copernicus mission dedicated to observing our atmosphere with high spatio-temporal resolution to be used for air quality and pollution, ozone and ultraviolet radiation, and climate monitoring and forecasting (ESA 2024b ). This satellite has a resolution of 1113.2 meters (1113.2m). Our study used Sentinel-5P data to spot CO, NO 2 , SO 2 , and CH 2 O concentrations over five years. Concertation values are presented from the lowest pixel to the highest pixel in colour: black, blue, purple, cyan, green, yellow, and red. 2.2.4. Google Earth Engine Google Earth Engine (GEE) is a machine learning cloud-based geospatial analysis platform launched in 2010 that enables users to visualize and analyse satellite images to oversee the environmental conditions of our planet. Scientists and other researchers or policymakers use it for remote sensing studies to predict a change or outbreak issues for the environment or natural capital and more for enhance better management (Gorelick et al. 2017 ; GEE 2024a ). In order to perform this analysis, GEE employed the Earth Engine Code Editor in a web-based integrated development environment (IDE) at " code.earthengine.google.com " for the Earth Engine JavaScript application programming interface (API) that users integrate codes and datasets that need to be analysed, then runs it and exports the results(GEE 2024b ). Data used in our study are summarized in Table 1 , inluding the minimum (min) and maximum (max) values. Table 1 Data and their sources used in this research Data Type Unit Min Max Source Study area map Shapefile - - - (Badan Informasi Geospasia 2024 ) LULC Waste Raster Weight - Tons - - - - (Karra et al. 2021 ) (SIPSN 2024 ) CO images mol/m 2 0 0.05 (ESA 2018a ) SO 2 images mol/m 2 0 0.0005 (ESA 2018b ) NO 2 images mol/m 2 0 0.0002 (ESA 2018c ) CH 2 O images mol/m 2 0 0.0003 (ESA 2018d ) 2.2.5. Methods Advancements in technology have created various tools that researchers and stakeholders employ to analyse complex data, identify potential problems, and find solutions for leaders, policymakers, and exposed individuals. One such tools is the Google Earth Engine (GEE), which has applications ranging from mapping mangrove forests (Yancho et al. 2020 ), addressing environmental issues such as air pollution and greenhouse gas emissions (Kinakh et al. 2017 ; Ghasempour et al. 2021 ; Ikram et al. 2022 ), as well as surveying land cover land change and others. Our study adopted a similar GEE approach to investigate the impact of waste and LULC on emissions of gases such as CO, NO 2 , SO 2 , and CH 2 O. We began our research by delineating a region of interest (ROI) using ArcGIS 10.8. We then added the ROI and brought in data from the Sentinel-5 Precursor satellite to the GEE coding editor to assess the situation of gas emissions in our area. After, we developed the codes in GEE to model the levels of CO, NO 2 , SO 2 , and CH 2 O from 2019 to 2023, using mean Eq. (1) to detect yearly concentrations (Gonzalez 2018 ) and a time series Eq. (2) to find monthly changes (GEE 2024c ). \(\:\text{M}\text{C}=\frac{1}{\text{N}}\sum\:_{\text{i}=0}^{\text{N}}\text{P}\text{i}\) Eq. (1) Where MC is mean concentration N is total number of pixels in region of interest Pi is the concentration value of the i-th pixel \(\:Pt=t0+t1\dots\:\dots\:tN\) Eq. (2) Where Pt represents a value of time series at time "t" t0 + t1 …… tN represents individual values at different time points ( 0 …..N) Further, we acquired LULC data from the Sentinel-2 and waste data from SIPSN Indonesia to assess whether their changes and increases over five years correlate with elevated levels of pollutants in our region of interest. An overview of our methodology is depicted in Fig. 2 . 3. RESULTS AND DISCUSSIONS Our study evaluated interannual concentration variability of four gases, CO, NO2, SO2, and CH2O, from January 2019 to December 2023. Additionally, we assessed the status of waste and LULC in our regions to identify the correlations with emissions observed in Bekasi, Jakarta, Bogor Tangerang, and Depok. Our analysis revealed a change in emission concentration levels emphasizing the existence of air pollution due to overseen alterations in LULC and an increase in waste, detailed in the following sections. 3.1. Waste Waste generation in Bekasi, Jakarta, Bogor, Tangerang, and Depok rose from 1,213,625 tons in 2019 to 2,179,386 tons in 2023. Specifically, Bogor City, Bekasi Regency, Depok City, and Tangerang regency experienced yearly increases in waste collection. However, there were gaps in disposal, improving only slightly from 97% in 2019 to 98.5% in 2023 of the total waste collected in these regions, as illustrated in Fig. 3 . Over the past five years, 169,050 tons of collected waste were not disposed of in designated landfills. Additionally, in 2023, 33.4% of waste in Indonesia was still mismanaged, as reported by the Environment and Forestry Minister reported in Indonesia Agency News (ANTARA 2023 ), underscoring the pressing impact on air pollution that we emphasised in this study. 3.2. Land Use and Land Cover changes Our findings indicate a substantial LULC change in the study area over the past five years, as depicted in Fig. 4 . In order to quantify these changes, we exported the relevant data from the map to a table, as shown in Table 2 . Table 2 LULC classes changes in Km 2 Classes 2018–2019 2019–2020 2020–2021 2021–2022 2022–2023 2023–2024 Water 129.27 125.05 142.16 144.08 141.26 131.05 Trees 1528.72 1463.14 1524.82 1527.33 1490.88 1443.84 Flooded vegetation 63.27 66.24 60.80 60.31 64.79 51.92 Crops 1872.97 1829.86 1814.28 1798.40 1785.19 1625.22 Built Area 3067.26 3153.28 3145.30 3182.69 3198.87 3379.03 Bare ground 8.22 6.78 4.56 4.53 5.00 6.59 Clouds 2.05 0.46 10.85 2.08 13.91 4.02 Rangeland 128.63 155.70 97.74 80.97 100.49 158.72 Both Fig. 4 and Table 2 demonstrated that built areas, water, rangeland and clouds have increased over the five years. In contrast, tree cover, flooded vegetation, and crops have decreased. Remarkably, the built-up area expanded prominently, reaching 180.16 Km 2 in 2023, a considerable rise compared to previous years, as shown in Fig. 5 . These findings suggest that the region has undergone rapid LULC alterations, impacting air quality. 3.3. Air pollution 3.3.1. Sulphur dioxide Sulphur dioxide (SO 2 ) is a colourless gas with a pungent odour that becomes liquid under pressure and readily dissolves in water (National Library of Medicine 2024 ). It is a severe air pollutant that emitted from both anthropogenic and natural sources, primarily through biomass burning, which releases sulphur dioxide or sulphur oxides (SO x ) depending on the composition of biomass (Khalaf et al. 2022 ; US EPA 2023b ). Numerous researches have also shown that SO 2 has proved also it is discharged from activities such as open waste burning, incinerations, and agricultural biomass burning, especially during land preparation and other LULC changes leading to air pollution (Permadi and Kim Oanh 2013 ; Mallongi et al. 2019 ; Ahmad et al. 2023 ; Firdaus et al. 2023 ). After executing our GEE model for SO 2 and overlaying the results onto our region of interest, we found colour variations where black represents the lowest concentration level of SO 2 and cyan indicates the highest. From 2019 to 2023, with 2022 the year that SO2 pollution has impacted the most extensive area in our study area as depicted in Fig. 6 . Furthermore, these results were undertaking analysis with ArcGIS10.8; we found that its emissions were lower in 2019, with concentration ranging from a minimum of -0.0000623 to a maximum of 0.000187 mol/m 2 , while the increase in 2020 with a range of -0.000165 to 0.000222 mol/m 2 , raised in 2021 with a range of -0.000135 to 0.000281 mol/m 2 , dramatically surpassing levels of 2021 with a range of -0.000199 to 0.000319 mol/m 2 in 2022 and declined in 2023 with a range of – 0.0000441 to 0.000224 mol/m 2 . The significant increase in 2022 is related to reopening many activities locked during Covid-19 lockdowns. Figure 7 illustrates these findings, with red denoting higher emissions of SO 2 and black denoting lower emissions. Notably, our time series results proved that 2023 had higher air pollution of SO 2 while 2020 recorded lower. Moreover, the highest value recorded over five years was 0.00037 mol/m 2 in 2022, while the lowest was − 0.00021 mol/m 2 in 2021. Furthermore, SO 2 emissions increased in May, June, July, and September over five years compared to the other months, as shown in Fig. 8 . This can be attributed to the summer season when people engage in much open burning, and there is no rain to cleanse the air. 3.3.2. Carbon monoxide Carbon monoxide (CO) is a highly poisonous, colourless, odourless, tasteless gas, and flammable in the air. It turns into liquid at lower temperatures and is insoluble in water above 70 o C. CO is primarily formed through incomplete combustion from natural processes or anthropogenic activities (Wilbur et al. 2012 ; Donald 2015 ). Waste releases CO through the decay of biomass (Stegenta-Dąbrowska et al. 2019 ) and the open burning of solid waste or biomass (Ramadan et al. 2022 ). Moreover, LULC changes contribute to CO emissions via deforestation, agricultural clearing, and urbanization, which reduce vegetation cover and alter landscapes. The findings that we got after running codes in GEE with the satellite range from 0 mol/m 2 (min) to 0.05 mol/m 2 (max). Yellow colour denotes the highest concentration, while cyan signifies the lowest. The data showed that 2019 had a higher concentration with more yellow colour than the subsequent years, predominated by green, as displayed in Fig. 9 . All the findings were then downloaded and uploaded into ArcGis10.8 to clarify CO emissions. The results revealed that CO concentration from 0.0285 to 0.0426 mol/m 2 in 2019, 0.0245 to 0.0354 mol/m 2 in 2020, 0.0231 to 0.0358 mol/m 2 in 2021, 0.0219 to 0.0341 mol/m 2 in 2022 and 0.0246 to 0.0405 mol/m 2 in 2023. These values were demonstrated in Fig. 10 , with black indicating lower and red indicating higher concentrations. Furthermore, the time series chart indicated that the highest emission recorded was 0.049 mol/m 2 in 2019 before Covid-19 and 0.048 mol/m 2 in 2023. Additionally, higher CO emissions were observed from August to October over the past five years, with a decrease starting in November, the second month of the rainy season, when rain begins to cleanse the air and reduce open burning, as revealed in Fig. 11 . 3.3.3. Formaldehyde hac Formaldehyde CH 2 O is colourless, pungent, highly flammable, irritating, and toxic gas with a low molecular weight in its pure form. It is soluble in water, alcohol, and other polar solvents. CH 2 O has strong electrophilic and reactive properties and the ability to polymerise, which can lead to the formation of explosive mixtures in the air. It can decompose into carbon monoxide and methanol at high temperatures and quickly oxidises to carbon dioxide in sunlight (Subasi 2020 ). All these properties make it widely used in many applications. However, it is emitted into the atmosphere through the combustion of biomass, decomposition, open burning, and incineration (Kaden et al. 2010 ), as a result of waste disposal or LULC changes. Our GEE model indicated a higher concentration of CH 2 O in 2019 and 2023, represented in red in Fig. 12 . After that, these findings were exported in ArcGIS 10.8, and we found that the CH 2 O concentration levels ranged from 0.000144 to 0.000396 mol/m 2 in 2019, lowed to 0.0000877 to 0.000315 mol/m 2 in 2020, and further to 0.0000735 to 0.000313 mol/m 2 in 2021. Nevertheless, the levels increased again to 0.0000778 to 0.000338 mol/m 2 in 2022 and 0.000123 to 0.000397 mol/m 2 in 2023. Figure 13 represents these outcomes, with black stipulating lower values of CH 2 O emissions and red stipulating higher values of CH 2 O emissions. Moreover, the time series chart results showed that the highest concentration levels were 0.00043 mol/m 2 in 2019 and 0.00038 mol/m 2 in 2023. Plus, a higher concertation of CH 2 O was observed from May to November for 2019 and 2023, while 2020, 2021, and 2022 did not rapidly increase due to Covid-19 lockdowns, which halted many businesses. The monthly CH 2 O emissions from 2019 to 2023 are displayed in Fig. 14 . 3.3.4. Nitrogen dioxide Nitrogen dioxide (NO 2 ) is a highly reactive gas with a pungent odour and a reddish orange-brown colour. It acts as an oxidising agent, is corrosive, and non-combustible(Wang 2024 ). NO 2 is emitted during combustion processes (Van Tran et al. 2020 ), from fireplaces, through the use of fertilizer inputs in agriculture, and from nitrogen-containing biomass decaying to release N 2 O (Ritchie et al. 2024 ), which oxidises into NO 2 in ambient conditions when there is presence of oxidising agents like O 2 , O 3 and volatile organic compound (VOCs) (Jarvis et al. 2010 ). Our outcomes denoted that high NO 2 emission regions were detected in 2019, 2021, 2022 and 2023, spanning from black colour, indicative of the lowest concentration, to yellow, indicative of the highest, as demonstrated by the GEE findings, as depicted in Fig. 15 . To further analyse NO 2 emission patterns, the newly acquired datasets were transferred to ArcGIS 10.8. The results revealed that NO 2 concentration levels ranged from 0.0000968 to 0.000175 mol/m 2 in 2019. These levels decreased to 0.0000974 to 0.000109 in 2020, rose again to 0.0000713 to 0.000169 mol/m 2 in 2021, varied between 0.0000385 to 0.000142 mol/m 2 in 2022 and increased to 0.000132 to 0.000166 mol/m 2 in 2023. These levels varied from black for lower emissions to red for higher emissions, as illustrated in Fig. 16 . Moreover, the time series analysis chart pointed out the highest NO2 concertation was 0.000135 mol/m 2 in 2019 and 0.000119 mol/m 2 in 2023. An increase was spotted from June to October in 2023. In the other years, higher concentration was noted in June, July, August and October, except for 2022, which signalled a decrease in October, as displayed in Fig. 17 . 3.4. Impact of waste and LULC on air pollution Our research findings demonstrated that the increase in waste generation over five years resulted in decaying, open burning, landfills and incineration as solutions to manage the overwhelming waste burden. Additionally, changes in LULC have been marked by the expansion of built-up areas, rangeland, rose deforestation, reduced vegetation cover and other cropland. This decreased forest and vegetation cover has led to widespread biomass decomposition and burning. Consequently, these factors collectively have elevated the levels of pollutants such as CO, SO 2 , NO 2 and CH 2 O levels in the ecosystem of Tangerang, Jakarta, Bogor, Bekasi and Depok over the past five years, as evidenced by increased emissions of these pollutants that we discussed in previous sections. Furthermore, cropland reduction has intensified crop production on limited land, necessitating the greater use of fertilizers and other chemicals to meet food demands. Consequently, these factors collectively have elevated the levels of pollutants such as CO, SO 2 , NO 2 and CH 2 O levels in the ecosystem of Tangerang, Jakarta, Bogor, Bekasi and Depok over the past five years, as evidenced by increased emissions of these pollutants that we discussed in previous sections. Nonetheless, we have also noted a temporary decrease in these pollutants during Covid-19 lockdowns, underscoring the impact of reduced anthropogenic activities on air quality. Thus, the escalation of these air pollutants has had severe long-lasting effects, including respiratory diseases and other health issues. As a fact, recent studies have suggested staggering statistics, with over 7,000 health adversities in children, 10,0000 deaths, and more than 5,000 hospitalizations in Jakarta annually due to air pollution, alongside economic cost estimated at USD 2,943.42 million to mitigate these health issues each year (Syuhada et al. 2023 ). Additionally, air pollution has exacerbated biotic stress, and contributed to climate change consequences such as El Niño, acid rain, increased temperature and sunshine, changes in windspeed and irregular rainfall (World Resources Institute 2023 ). Conclusion Addressing air pollution remains a critical global challenge aggravated by anthropogenic activities, including waste and LULC changes that release CO, SO 2 , NO 2 and CH 2 O pollutants through processes like open burning, incineration, organic decomposition and use of fertilizers. Our study has identified a pressing alteration in these driving factors from 2019 to 2023, contributing to rising air pollution levels of CO, SO 2 , NO 2 and CH 2 O over this period, except 2020 and 2021 due to COVID-19 lockdowns. These findings underscore the influence of waste and LULC changes on air quality in the studied regions. For this reason, it is imperative to implement proactive measures to mitigate air pollution. We firmly advocate for a participatory, compressive approach to waste management and sustainable land management practices. These strategies include promoting the principles of reuse, recycle and reduce (3R), enforcing a ban on biomass and open burning; implementing reforestation, green gardening, and agroforestry; establishing modern green urban villages with multistorey buildings, and encouraging sustainable composting and use of organic manure as fertilizer to improve air quality and public health in Tangerang, Jakarta, Bogor, Bekasi and Depok. Declarations Author Contribution 1.Oscar Umwanzisiwemuremyi: Conducted the research, analysed the data, and drafted the manuscript.2. Dr. Abidin Zaenal: Served as the primary supervisor, providing guidance, revisions, and final approval of the manuscript3. Dr. Yudi Setiawan: Acted as the co-supervisor, contributing guidance, revisions, and final approval of the manuscript. Data Availability "All data on the Waste, Land cover and Land use changes, CO, H2CO, NO2 and SO2 that support the findings of this study are included within this paper: and as per below https://drive.google.com/drive/folders/1EZQ1Vr6xuuhXKHmr50pFJUNYcdnNRWpW?usp=sharing" References Ahmad MN, Shao Z, Javed A. 2023. Modelling land use/land cover (LULC) change dynamics, future prospects, and its environmental impacts based on geospatial data models and remote sensing data. Environ Sci Pollut Res Int . 30(12):32985–33001. doi:10.1007/S11356-022-24442-2. ANTARA. 2023. Govt highlights strategic role of waste banks in Indonesia. [accessed 2024 Jun. 1]. https://en.antaranews.com/news/284949/govt-highlights-strategic-role-of-waste-banks-in-indonesia. Badan Informasi Geospasia. 2024. Tanah air indonesia (homeland). [accessed 2024 Feb. 27]. https://tanahair.indonesia.go.id/portal-web. Badan Pusat Statistik. 2024. Number of Population by Regency/Municipality for Jawa Barat, Banten province and DKI Jakarta 2022 - 2024. [accessed 2024 May 24]. https://www.bps.go.id/id. Crippa, M., Guizzardi, D., Pagani, F., Banja, M., Muntean, M., Schaaf E., Becker, W., Monforti-Ferrario, F. Q, R., Risquez Martin, A., Taghavi-Moharamli, P., Köykkä, J., Grassi, G., Rossi, S., Brandao De Melo, J., Oom, D., Branco, A., San-Miguel J, Vignati E. 2023. GHG emissions of all world countries. Publications Office of the European Union, Luxembourg. Donald JA. 2015 Jan 1. Carbon Monoxide. Handb Horm Comp Endocrinol Basic Clin Res ., forthcoming. ESA. 2018a. Sentinel-5P OFFL CO: Offline Carbon Monoxide | Earth Engine Data Catalog | Google for Developers. [accessed 2024 May 29]. https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CO. ESA. 2018b. Sentinel-5P OFFL SO2: Offline Sulfur Dioxide | Earth Engine Data Catalog | Google for Developers. [accessed 2024 May 28]. https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_SO2. ESA. 2018c. Sentinel-5P OFFL NO2: Offline Nitrogen Dioxide | Earth Engine Data Catalog | Google for Developers. [accessed 2024 May 30]. https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_NO2. ESA. 2018d. Sentinel-5P OFFL HCHO: Offline Formaldehyde | Earth Engine Data Catalog | Google for Developers. [accessed 2024 May 26]. https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_HCHO. ESA. 2024a. Copernicus Sentinel-2 mission and application. [accessed 2024 May 27]. https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-2. ESA. 2024b. Sentinel-5P and its applications. [accessed 2024 May 27]. https://sentiwiki.copernicus.eu/web/sentinel-5p. Firdaus FM, Elliott B, Malsch J, Surjadi P. 2023. 7 Things to Know About Jakarta’s Air Pollution Crisis. [accessed 2024 May 31]. https://wri-indonesia.org/en/insights/7-things-know-about-jakartas-air-pollution-crisis. Ge M, Friedrich J, Vigna L. 2020. 4 Charts Explain Greenhouse Gas Emissions by Countries and Sectors. GEE. 2024a. Google Earth Education. [accessed 2024 May 29]. https://www.google.com/earth/education/tools/google-earth-engine/. GEE. 2024b. Platform. [accessed 2024 May 29]. https://earthengine.google.com/platform/. GEE. 2024c. Time Series Modeling for GEE for Developers. [accessed 2024 May 29]. https://developers.google.com/earth-engine/tutorials/community/time-series-modeling. Ghasempour F, Sekertekin A, Kutoglu SH. 2021. Google Earth Engine based spatio-temporal analysis of air pollutants before and during the first wave COVID-19 outbreak over Turkey via remote sensing. J Clean Prod . 319:128599–128599. doi:10.1016/J.JCLEPRO.2021.128599. Gonzalez RC. 2018. Support Package for Digital Image Processing. [accessed 2024 May 30]. www.ImageProcessingPlace.com. Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R. 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens Environ . 202:18–27. doi:10.1016/J.RSE.2017.06.031. Ikram NM, Afifah L, Arthatia BS, Wicaksono SJ, Maharani M, Ediyanto, Ihsanudin T, Apriyanti D. 2022. Monitoring CO and O3concentration that caused climate change periodically using Google Earth Engine (study case: Java Island). IOP Conf Ser Earth Environ Sci . 1047(1). doi:10.1088/1755-1315/1047/1/012021. Jarvis DJ, Adamkiewicz G, Heroux M-E, Rapp R, Kelly FJ. 2010. Nitrogen dioxide. [accessed 2024 Jun. 4]. https://www.ncbi.nlm.nih.gov/books/NBK138707/. Kaden DA, Mandin C, Nielsen GD, Wolkoff P. 2010. Formaldehyde. [accessed 2024 Jun. 3]. https://www.ncbi.nlm.nih.gov/books/NBK138711/. Karra K, Kontgis C, Statman-Weil Z, Mazzariello JC, Mathis M, Brumby SP. 2021. GLOBAL LAND USE/LAND COVER WITH SENTINEL 2 AND DEEP LEARNING. Int Geosci Remote Sens Symp . 2021-July:4704–4707. doi:10.1109/IGARSS47720.2021.9553499. Khalaf EM, Mohammadi MJ, Sulistiyani S, Ramírez-Coronel AA, Kiani F, Jalil AT, Almulla AF, Asban P, Farhadi M, Derikondi M. 2022 Dec 22. Effects of sulfur dioxide inhalation on human health: a review. Rev Environ Health ., forthcoming. Kinakh V, Bun R, Danylo O. 2017. Geoinformation technology of analysis and vizualization of spatial data on greenhouse gas emissions using Google Earth Engine. Proc 12th Int Sci Tech Conf Comput Sci Inf Technol CSIT 2017 . 1 September:212–215. doi:10.1109/STC-CSIT.2017.8098771. Mallongi A, Safiu D, Amqam H, Syam A, Hatta M, Sutarti T, Abdul M, Siyoto S, Apollo A. 2019. Modelling of SO2and CO Pollution Due to Industry PLTD Emission Tello in Makassar Indonesia. ARPN J Eng Appl Sci . 14(2):634–640. doi:10.36478/JEASCI.2019.634.640. Manisalidis I, Stavropoulou E, Stavropoulos A, Bezirtzoglou E. 2020. Environmental and Health Impacts of Air Pollution: A Review. Front Public Heal . 8:14. doi:10.3389/FPUBH.2020.00014. Ministry of Environment and Forestry. 2021. Indonesia Third Biennial Update Report. Repub Indones ., forthcoming. National Library of Medicine. 2024. Sulfur Dioxide compound. [accessed 2024 May 31]. https://pubchem.ncbi.nlm.nih.gov/compound/Sulfur-Dioxide#section=Entrez-Crosslinks. Our World In Data. 2024. Community Emissions Data System"Emissions of Worldwide air pollutants CO,NOx,SO2 and non-CH4 VOCs 2019 to 2022. [accessed 2024 May 23]. https://ourworldindata.org/grapher/long-run-air-pollution?time=2019..latest. Permadi DA, Kim Oanh NT. 2013. Assessment of biomass open burning emissions in Indonesia and potential climate forcing impact. Atmos Environ . 78:250–258. doi:10.1016/J.ATMOSENV.2012.10.016. Popescu F, Ionel I, Popescu F, Ionel I. 2010 Aug 18. Anthropogenic Air Pollution Sources. Air Qual ., forthcoming. Ramadan BS, Rachman I, Matsumoto T. 2022. Activity and emission inventory of open waste burning at the household level in developing countries: a case study of Semarang City. J Mater Cycles Waste Manag . 24(3):1194. doi:10.1007/S10163-022-01371-3. Ritchie H. 2020. Sector by sector: where do global greenhouse gas emissions come from? - Our World in Data. OurWorldInData.org ., forthcoming. [accessed 2024 Jun. 4]. https://ourworldindata.org/ghg-emissions-by-sector#article-citation. Ritchie H, Rosado P, Roser M. 2024 Jan 5. Breakdown of carbon dioxide, methane and nitrous oxide emissions by sector. Our World Data ., forthcoming. [accessed 2024 Jun. 4]. https://ourworldindata.org/emissions-by-sector. SIPSN. 2024. Waste corrected and disposed to TPA for 2019 - 2023. [accessed 2024 May 27]. https://sipsn.menlhk.go.id/sipsn/. Stegenta-Dąbrowska S, Drabczyński G, Sobieraj K, Koziel JA, Białowiec A. 2019. The Biotic and Abiotic Carbon Monoxide Formation During Aerobic Co-digestion of Dairy Cattle Manure With Green Waste and Sawdust. Front Bioeng Biotechnol . 7:485662. doi:10.3389/FBIOE.2019.00283/BIBTEX. Subasi NT. 2020 Jan 24. Formaldehyde Advantages and Disadvantages: Usage Areas and Harmful Effects on Human Beings. Biochem Toxicol - Heavy Met Nanomater ., forthcoming. Syuhada G, Akbar A, Hardiawan D, Pun V, Darmawan A, Heryati SHA, Siregar AYM, Kusuma RR, Driejana R, Ingole V, et al. 2023. Impacts of Air Pollution on Health and Cost of Illness in Jakarta, Indonesia. Int J Environ Res Public Health . 20(4). doi:10.3390/IJERPH20042916/S1. Van Tran V, Park D, Lee YC. 2020. Indoor Air Pollution, Related Human Diseases, and Recent Trends in the Control and Improvement of Indoor Air Quality. Int J Environ Res Public Health . 17(8). doi:10.3390/IJERPH17082927. UNEP. 2023. Emissions Gap Report 2023: Broken Record – Temperatures hit new highs, yet world fails to cut emissions (again). US EPA. 2023a. Hazardous Air Pollutants: Sources and Exposure. [accessed 2024 May 23]. https://www.epa.gov/haps/hazardous-air-pollutants-sources-and-exposure. US EPA. 2023b. Overview of Sulfur Dioxide (SO2) Air Quality in the United States. https://www.epa.gov/system/files/documents/2022-08/SO2_2021.pdf. Wang NCY. 2024. Nitrogen dioxide (formerly nitrogen oxides). Encycl Toxicol Fourth Ed Vol 1-9 . 6:883–889. doi:10.1016/B978-0-12-824315-2.00756-9. WHO. 2021. Air pollution. In: Compendium of WHO and other UN guidance on health and environment. World Health Organization. [accessed 2024 May 23]. https://cdn.who.int/media/docs/default-source/who-compendium-on-health-and-environment/who_compendium_chapter2_01092021.pdf?sfvrsn=14f84896_5. Wilbur S, Williams M, Williams R, Scinicariello F, Klotzbach JM, Diamond GL, Citra M. 2012. Toxicological Profile for Carbon Monoxide. US Agency Toxic Subst Dis Regist . June:1–347. [accessed 2024 Jun. 3]. https://www.ncbi.nlm.nih.gov/books/NBK153693/. World Resources Institute. 2023. Southeast Asian Cities Have Some of the Most Polluted Air in the World. El Niño Is Making it Worse. [accessed 2024 Jun. 5]. https://www.wri.org/insights/air-pollution-southeast-asia-cities-jakarta-el-nino. Yancho JMM, Jones TG, Gandhi SR, Ferster C, Lin A, Glass L. 2020. The google earth engine mangrove mapping methodology (Geemmm). Remote Sens . 12(22):1–35. doi:10.3390/rs12223758. YUE XL, GAO QX. 2018. Contributions of natural systems and human activity to greenhouse gas emissions. Adv Clim Chang Res . 9(4):243–252. doi:10.1016/j.accre.2018.12.003. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4932823","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":358859511,"identity":"d67576ec-ba97-476a-8b23-bb27a2e8aa5e","order_by":0,"name":"OSCAR UMWANZISIWEMUREMYI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYPACCQj1gYEhAUoTqYVxBkQLiCYSMPMQo0V3Ru7DRzfbLOTk2w8/+2zbZpfHz97A2FyBR4vZjXRj49w2CWODM2nGs3Pbkoslew4wNp7BqyWNTRqoJXGDBIMxc24bc+KGGwnsDxvwa2H/DdIyfwb7Z2bLtnqQFsZGAlrYmEFaGm7wGDMzth0mQsuZZ8zSOedAfskpZuw5dzxxZs/BRvxajqcxfs4pqwOG2PHNDD/KqhP72ZsP4tUCBoxsKAxGghqA4A8GYxSMglEwCkYBAgAA+cZPDF8Mj3oAAAAASUVORK5CYII=","orcid":"","institution":"Rwanda Cultural Heritage Academy","correspondingAuthor":true,"prefix":"","firstName":"OSCAR","middleName":"","lastName":"UMWANZISIWEMUREMYI","suffix":""},{"id":358859512,"identity":"a81437a9-3305-4baa-b1df-2a68e7934d63","order_by":1,"name":"ZAENAL ABIDIN","email":"","orcid":"","institution":"IPB University","correspondingAuthor":false,"prefix":"","firstName":"ZAENAL","middleName":"","lastName":"ABIDIN","suffix":""},{"id":358859513,"identity":"6164d90c-2a3a-4c21-bd4a-b3c936f9ed71","order_by":2,"name":"YUDI SETIAWAN","email":"","orcid":"","institution":"IPB University","correspondingAuthor":false,"prefix":"","firstName":"YUDI","middleName":"","lastName":"SETIAWAN","suffix":""}],"badges":[],"createdAt":"2024-08-18 09:52:26","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4932823/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4932823/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66940343,"identity":"11a6f6ca-5a16-43c0-9a58-5cc664bc608f","added_by":"auto","created_at":"2024-10-18 08:48:34","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":148868,"visible":true,"origin":"","legend":"\u003cp\u003eRegion of interest\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4932823/v1/6bc20bbd3f0e26caa7bba518.jpeg"},{"id":66940344,"identity":"49cdf36d-d03b-4aba-b84f-e5dcf5dd82be","added_by":"auto","created_at":"2024-10-18 08:48:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":78321,"visible":true,"origin":"","legend":"\u003cp\u003eMethodology flowchart\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4932823/v1/4b9444ca1af635762b9cf44b.png"},{"id":66940342,"identity":"86468a64-23e1-4786-ba61-4dfd9c7dc16e","added_by":"auto","created_at":"2024-10-18 08:48:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":25728,"visible":true,"origin":"","legend":"\u003cp\u003eWaste in study area\u003cem\u003e (\u003c/em\u003eAdapted from\u003cstrong\u003e \u003c/strong\u003e(SIPSN 2024)\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4932823/v1/d2f9a5e1471461e1db3b855a.png"},{"id":66941603,"identity":"5ad00320-763f-436e-9010-0d53f21d8d6c","added_by":"auto","created_at":"2024-10-18 08:56:34","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":224676,"visible":true,"origin":"","legend":"\u003cp\u003eLULC classes changes 2019 to 2023\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4932823/v1/eb49e0d0ac692eb26efa8438.jpeg"},{"id":66941604,"identity":"bbd60d0d-2924-41a0-8acd-159a9bfc8ee1","added_by":"auto","created_at":"2024-10-18 08:56:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":15003,"visible":true,"origin":"","legend":"\u003cp\u003eLand cover variations from 2019 to 2023\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4932823/v1/ee229525bc5cc295fd9fd0b6.png"},{"id":66940353,"identity":"1ef12b00-aa31-4769-a92c-1695e44ec243","added_by":"auto","created_at":"2024-10-18 08:48:34","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":134323,"visible":true,"origin":"","legend":"\u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e GEE outputs colors representing concetration changes from 2019 to 2023\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4932823/v1/d14e4f1bcb2cc76bbea843ee.jpeg"},{"id":66940352,"identity":"ba856fa2-243a-4ed9-b0ae-053d81b8a855","added_by":"auto","created_at":"2024-10-18 08:48:34","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":160098,"visible":true,"origin":"","legend":"\u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e emissions from 2019 to 2023\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4932823/v1/67e4cad55635ae2669a6add7.jpeg"},{"id":66940357,"identity":"1198086e-1b7f-4d2d-97dc-3f012e65a934","added_by":"auto","created_at":"2024-10-18 08:48:34","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":103536,"visible":true,"origin":"","legend":"\u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e monthly concentration changes\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4932823/v1/00828982dcde3d0b7595a41f.png"},{"id":66941886,"identity":"52660fdd-29f9-498d-b90a-63a6fbc21281","added_by":"auto","created_at":"2024-10-18 09:04:34","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":86042,"visible":true,"origin":"","legend":"\u003cp\u003eCO emitted levels from 2019 to 2023\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4932823/v1/162e32d25d692a8cb693ff2c.jpeg"},{"id":66941885,"identity":"b6157a8d-ddf6-4f3f-b6c1-0fd1d5fdc62c","added_by":"auto","created_at":"2024-10-18 09:04:34","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":106270,"visible":true,"origin":"","legend":"\u003cp\u003eCO concentration changes 2019 to 2023\u003c/p\u003e","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4932823/v1/2ed98ee37a32d6dd8d42806d.jpeg"},{"id":66941887,"identity":"6e5ea189-af61-471c-b382-4673b4291743","added_by":"auto","created_at":"2024-10-18 09:04:34","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":79884,"visible":true,"origin":"","legend":"\u003cp\u003eCO monthly concentration\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-4932823/v1/8bf5bd1a22a11023fa7b560b.png"},{"id":66941601,"identity":"0cb7e64e-2a81-45a8-acdf-5501c7a77901","added_by":"auto","created_at":"2024-10-18 08:56:34","extension":"jpeg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":116619,"visible":true,"origin":"","legend":"\u003cp\u003eCH\u003csub\u003e2\u003c/sub\u003eO density gradients derived from GEE outcomes\u003c/p\u003e","description":"","filename":"floatimage12.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4932823/v1/8a483a30f6dfb00c749b8c49.jpeg"},{"id":66940349,"identity":"235dce90-af4a-4c41-8167-b4854a6c95ca","added_by":"auto","created_at":"2024-10-18 08:48:34","extension":"jpeg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":126522,"visible":true,"origin":"","legend":"\u003cp\u003eCH\u003csub\u003e2\u003c/sub\u003eO distribution patterns over five years\u003c/p\u003e","description":"","filename":"floatimage13.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4932823/v1/f772b70a34a127b3a691b6ab.jpeg"},{"id":66940358,"identity":"328c64df-b897-481d-adfd-0ab766ecf516","added_by":"auto","created_at":"2024-10-18 08:48:34","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":89534,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly changes of CH\u003csub\u003e2\u003c/sub\u003eO emissions\u003c/p\u003e","description":"","filename":"floatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-4932823/v1/30a598d7eb9482546684948e.png"},{"id":66941607,"identity":"f1c6728b-9f14-4337-a9b0-553dd0f079b6","added_by":"auto","created_at":"2024-10-18 08:56:34","extension":"jpeg","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":121953,"visible":true,"origin":"","legend":"\u003cp\u003eAlterations in NO\u003csub\u003e2\u003c/sub\u003e emissions observed within GEE\u003c/p\u003e","description":"","filename":"floatimage15.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4932823/v1/4c0ebc29a5cd9af8a7ec0e20.jpeg"},{"id":66940348,"identity":"690fc370-17de-4af2-96be-aa0567efb9a2","added_by":"auto","created_at":"2024-10-18 08:48:34","extension":"jpeg","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":122096,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial spread of NO2 pollution from 2019 to 2023\u003c/p\u003e","description":"","filename":"floatimage16.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4932823/v1/0e15099a4f19a3ee43ea6130.jpeg"},{"id":66940359,"identity":"a8e02fbc-efeb-486d-88c3-361290b1ca3b","added_by":"auto","created_at":"2024-10-18 08:48:34","extension":"png","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":82113,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly fluctuations in NO\u003csub\u003e2\u003c/sub\u003e concentration\u003c/p\u003e","description":"","filename":"floatimage17.png","url":"https://assets-eu.researchsquare.com/files/rs-4932823/v1/2e9c88ed10ef96c3898ea83b.png"},{"id":73023390,"identity":"65dc074c-e8d8-432a-88e3-658099243d73","added_by":"auto","created_at":"2025-01-06 04:23:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2343652,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4932823/v1/b58605e1-5d74-42e3-b2ec-01f520c9fad4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigating Waste and Land Use Changes Effects on Air Pollution Using Google Earth Engine in Jakarta Metropolitan in Indonesia","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eAir pollution is the release of harmful substances into the atmosphere, adversely impacting living organisms (Manisalidis et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These substances may originate from natural sources such as volcanic eruptions, forest fires, sea salt, desert dust (for example in Sahara), and natural decomposition of plants and animals, as well as from anthropogenic activities including transportation, land use and land cover change (LULC), waste, industry processes, energy production, open burning, and smoking (Popescu et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; WHO \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; US EPA \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). The primary air pollutants include carbon monoxide (CO), carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e), methane (CH\u003csub\u003e4\u003c/sub\u003e), sulphur dioxide (SO\u003csub\u003e2\u003c/sub\u003e), nitrous oxide (NO\u003csub\u003ex\u003c/sub\u003e), particulate matter (PM), and non-methane volatile organic compounds (VOCs)(Popescu et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Manisalidis et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). According to the world data, global emissions of CO were 443.18\u0026nbsp;million tonnes in 2019, decreasing to 427.75\u0026nbsp;million tonnes in 2020, 428.15\u0026nbsp;million tonnes in 2021, and 423.30\u0026nbsp;million tonnes in 2022. NO\u003csub\u003ex\u003c/sub\u003e emissions were 96.34\u0026nbsp;million tonnes in 2019, 89.99\u0026nbsp;million tonnes in 2020, 92.49\u0026nbsp;million tonnes in 2021, and 91.90\u0026nbsp;million tonnes in 2022. SO\u003csub\u003e2\u003c/sub\u003e emissions were 70.48\u0026nbsp;million tonnes in 2019, 66.19\u0026nbsp;million tonnes in 2020, 68.45\u0026nbsp;million tonnes in 2021, and 69.31\u0026nbsp;million tonnes in 2022. While non-methane VOC emissions were 133.52\u0026nbsp;million tonnes in 2019, 131.08\u0026nbsp;million tonnes in 2020, 131.35\u0026nbsp;million tonnes in 2021, and 130.79\u0026nbsp;million tonnes in 2022 (Our World In Data \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, these pollutants are significant contributors to greenhouse gas emissions, which were approximately 29.07\u0026nbsp;billion metric tonnes of CO\u003csub\u003e2\u003c/sub\u003e-equivalent (CO2-eq) from natural sources in 2016 compared to 49.4\u0026nbsp;billion metric tonnes of CO\u003csub\u003e2\u003c/sub\u003e-eq from anthropogenic activities during the same year (YUE and GAO \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ritchie \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thus, these anthropogenic sources played a part in global gas emissions as follows: energy at 73.2%; agriculture, forest, and land use at 18.4%; chemical and cement industries coupled with 5.2%; and waste at 3.2% (Ritchie \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Despite a temporary reduction in gas emissions to 54.5\u0026nbsp;billion metric tonnes of CO\u003csub\u003e2\u003c/sub\u003e-eq in 2020 due to the COVID-19 pandemic lockdowns and economic downturn, emissions rebounded dramatically after that to 56.8\u0026nbsp;billion metric tonnes in 2021, 57.4\u0026nbsp;billion metric tonnes of CO\u003csub\u003e2\u003c/sub\u003e-eq, 57.4\u0026nbsp;billion metric tonnes of CO\u003csub\u003e2\u003c/sub\u003e-eq in 2022. The United Nations Environment Programme (UNEP) predicts emissions could reach 60\u0026nbsp;billion metric tonnes of CO\u003csub\u003e2\u003c/sub\u003e-eq by 2030 if the current policies persist and further exacerbate air pollution issues (UNEP \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consequently, this air pollution is leading to 9\u0026nbsp;million human deaths annually and environmental problems such as wildlife loss, eutrophication, acid rain, global warming, and climate change (Manisalidis et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, emissions contributions vary by country, with the top 10 emitters accounting for 66.65% of global emissions (Ge et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), including Indonesia, for instance, 1.865\u0026nbsp;billion metric tonnes of CO\u003csub\u003e2\u003c/sub\u003e-eq in 2019, 1.10471\u0026nbsp;billion metric tonnes of CO\u003csub\u003e2\u003c/sub\u003e-eq in 2020, followed by 1.12806\u0026nbsp;billion metric tonnes of CO\u003csub\u003e2\u003c/sub\u003e-eq in 2021 and 1.24083\u0026nbsp;billion metric tonnes of CO\u003csub\u003e2\u003c/sub\u003e-eq in 2022, predominantly from CO\u003csub\u003e2\u003c/sub\u003e, CH\u003csub\u003e4\u003c/sub\u003e, N\u003csub\u003e2\u003c/sub\u003eO, and Fluorinated gases (F-gases), and this is projected to reach 2.868\u0026nbsp;billion metric tonnes of CO\u003csub\u003e2\u003c/sub\u003e-eq in 2030, as the leading pollutants, discharged from the following sources: agriculture with 15.49%, building 3.06%, fuel exploitation 21.38%, industrial combustion 14.68%, power industries: 20.44%, processes 5.8%, transport 11.74% and waste 7.72% per data of 2022 (Ministry of Environment and Forestry \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Crippa, \u003cem\u003eet al.\u003c/em\u003e 2023). For this reason, our research aimed to shed light on the contribution of waste and LULC changes to air pollution utilizing Excel analysis for trend changes for waste, unsupervised image analysis for LULC and Google Earth Engine (GEE) to model missions of CO, NO\u003csub\u003e2\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e2\u003c/sub\u003eO over five years (2019\u0026ndash;2023) in the Jakarta Metropolitan region in Indonesia. Our findings revealed significant LULC changes from 2019 to 2023, with built areas increasing while trees, flooded vegetation, and crops decreased. In addition, waste in our study area rose from 2019 to 2023 and was mismanaged. As a result, we found that the gas emissions in these regions exceeded the minimum and approached the maximum thresholds detected by the Sentinel-5 Precursor satellite. CO, NO\u003csub\u003e2\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e2\u003c/sub\u003eO concentrations fluctuated between 2019 and 2023. Therefore, we stressed that measures to reduce air pollution should also consider curbing waste generation and mismanagement, deforestation, and promoting sustainable land use. Moreover, we recommend reforestation, greening gardens, agroforestry, and green urban building with a focus on multi-storey buildings to mitigate air pollution in the Jakarta Metropolitan region in Indonesia.\u003c/p\u003e"},{"header":"2. METHODOLOGY","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Area\u003c/h2\u003e \u003cp\u003eThis study was conducted between November 2023 and May 2024 in Jakarta, the capital of Indonesia, and its surrounding and satellite cities and regencies, including Tangerang (Regency and City), Bogor (Regency and City), Bekasi (Regency and City) and Depok City. This region covers an area of 6800.40 Km2, situated between longitudes 106\u0026deg;20'0\"E and 107\u0026deg;20'0\"E, and latitudes 6\u0026deg;45'0\"S and 5\u0026deg;30'0\"S, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. As data of 2023, this region has a population of 32,143,225 (Badan Pusat Statistik \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These cities are primarily characterized by administration, business activities, tourism, and industries, while the regencies are predominantly focused on agriculture, tourism, and minor-scales businesses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data Acquisition\u003c/h2\u003e \u003cp\u003eDuring our study, we utilize a variety of sources and websites, detailed below, to get our data and to conduct our analyses.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. National Waste Management Information System\u003c/h2\u003e \u003cp\u003eThe National Waste Management Information System Indonesia, referred to as Sistem Informasi Pengelolaan Sampah Nasional (SIPSN) is a web-based database that has been in operation since 2018 with the purpose of storing and managing data for waste management in Indonesia. Therefore, in this study, we retrieved waste data that has reached the final disposal sites known as Tempant Tempat Pemrosesan Akhir (TPA), focusing on both cities and regencies of Tangerang, Bogor, Bekasi, the city of Depok, and the capital of Jakarta known as Daerah Khusus Ibukota Jakarta(DKI Jakarta) (SIPSN \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This data covers corrected and managed waste information from 2019 to 2023.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Sentinel-2\u003c/h2\u003e \u003cp\u003eThe Sentinel-2 satellite, operated by the European Space Agency (ESA), was launched on 23rd June 2015 under the Copernicus Sentinel-2 mission, and updated in 2017 and 2024 to monitoring the land and vegetation changes (ESA \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). This satellite has a resolution of ten meters (10m). It categorises land and vegetation changes into nine classes: one as water, two as trees, four as flooded vegetation, five as crops, seven as built area, eight as bare ground, nine as snow, ten as clouds and eleven as rangeland (Karra et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Then, we downloaded pixel data from 2018/2019 to 2023/2024 to detect LULC changes in our regions during these years.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3. Sentinel-5P\u003c/h2\u003e \u003cp\u003eThe Sentinel-5P satellite, also managed by ESA, was sent into orbit on 13th October 2017 in collaboration with the Netherlands Space Office and others under the Copernicus mission dedicated to observing our atmosphere with high spatio-temporal resolution to be used for air quality and pollution, ozone and ultraviolet radiation, and climate monitoring and forecasting (ESA \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). This satellite has a resolution of 1113.2 meters (1113.2m). Our study used Sentinel-5P data to spot CO, NO\u003csub\u003e2\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, and CH\u003csub\u003e2\u003c/sub\u003eO concentrations over five years. Concertation values are presented from the lowest pixel to the highest pixel in colour: black, blue, purple, cyan, green, yellow, and red.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4. Google Earth Engine\u003c/h2\u003e \u003cp\u003eGoogle Earth Engine (GEE) is a machine learning cloud-based geospatial analysis platform launched in 2010 that enables users to visualize and analyse satellite images to oversee the environmental conditions of our planet. Scientists and other researchers or policymakers use it for remote sensing studies to predict a change or outbreak issues for the environment or natural capital and more for enhance better management (Gorelick et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; GEE \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). In order to perform this analysis, GEE employed the Earth Engine Code Editor in a web-based integrated development environment (IDE) at \"\u003cb\u003ecode.earthengine.google.com\u003c/b\u003e\" for the Earth Engine JavaScript application programming interface (API) that users integrate codes and datasets that need to be analysed, then runs it and exports the results(GEE \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). Data used in our study are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, inluding the minimum (min) and maximum (max) values.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData and their sources used in this research\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy area map\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShapefile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(Badan Informasi Geospasia \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLULC\u003c/p\u003e \u003cp\u003eWaste\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaster\u003c/p\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003eTons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(Karra et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e(SIPSN \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eimages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emol/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(ESA \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eimages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emol/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(ESA \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eimages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emol/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(ESA \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018c\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCH\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eimages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emol/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(ESA \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018d\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.5. Methods\u003c/h2\u003e \u003cp\u003eAdvancements in technology have created various tools that researchers and stakeholders employ to analyse complex data, identify potential problems, and find solutions for leaders, policymakers, and exposed individuals. One such tools is the Google Earth Engine (GEE), which has applications ranging from mapping mangrove forests (Yancho et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), addressing environmental issues such as air pollution and greenhouse gas emissions (Kinakh et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ghasempour et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ikram et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), as well as surveying land cover land change and others. Our study adopted a similar GEE approach to investigate the impact of waste and LULC on emissions of gases such as CO, NO\u003csub\u003e2\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, and CH\u003csub\u003e2\u003c/sub\u003eO. We began our research by delineating a region of interest (ROI) using ArcGIS 10.8. We then added the ROI and brought in data from the Sentinel-5 Precursor satellite to the GEE coding editor to assess the situation of gas emissions in our area. After, we developed the codes in GEE to model the levels of CO, NO\u003csub\u003e2\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, and CH\u003csub\u003e2\u003c/sub\u003eO from 2019 to 2023, using mean Eq.\u0026nbsp;(1) to detect yearly concentrations (Gonzalez \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and a time series Eq.\u0026nbsp;(2) to find monthly changes (GEE \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024c\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\text{M}\\text{C}=\\frac{1}{\\text{N}}\\sum\\:_{\\text{i}=0}^{\\text{N}}\\text{P}\\text{i}\\)\u003c/span\u003e \u003c/span\u003e Eq.\u0026nbsp;(1)\u003c/p\u003e \u003cp\u003eWhere MC is mean concentration\u003c/p\u003e \u003cp\u003eN is total number of pixels in region of interest\u003c/p\u003e \u003cp\u003ePi is the concentration value of the i-th pixel\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:Pt=t0+t1\\dots\\:\\dots\\:tN\\)\u003c/span\u003e \u003c/span\u003e Eq.\u0026nbsp;(2)\u003c/p\u003e \u003cp\u003eWhere Pt represents a value of time series at time \"t\"\u003c/p\u003e \u003cp\u003et0\u0026thinsp;+\u0026thinsp;t1 \u0026hellip;\u0026hellip; tN represents individual values at different time points ( 0 \u0026hellip;..N)\u003c/p\u003e \u003cp\u003eFurther, we acquired LULC data from the Sentinel-2 and waste data from SIPSN Indonesia to assess whether their changes and increases over five years correlate with elevated levels of pollutants in our region of interest. An overview of our methodology is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. RESULTS AND DISCUSSIONS","content":"\u003cp\u003eOur study evaluated interannual concentration variability of four gases, CO, NO2, SO2, and CH2O, from January 2019 to December 2023. Additionally, we assessed the status of waste and LULC in our regions to identify the correlations with emissions observed in Bekasi, Jakarta, Bogor Tangerang, and Depok. Our analysis revealed a change in emission concentration levels emphasizing the existence of air pollution due to overseen alterations in LULC and an increase in waste, detailed in the following sections.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Waste\u003c/h2\u003e \u003cp\u003eWaste generation in Bekasi, Jakarta, Bogor, Tangerang, and Depok rose from 1,213,625 tons in 2019 to 2,179,386 tons in 2023. Specifically, Bogor City, Bekasi Regency, Depok City, and Tangerang regency experienced yearly increases in waste collection. However, there were gaps in disposal, improving only slightly from 97% in 2019 to 98.5% in 2023 of the total waste collected in these regions, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOver the past five years, 169,050 tons of collected waste were not disposed of in designated landfills. Additionally, in 2023, 33.4% of waste in Indonesia was still mismanaged, as reported by the Environment and Forestry Minister reported in Indonesia Agency News (ANTARA \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), underscoring the pressing impact on air pollution that we emphasised in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Land Use and Land Cover changes\u003c/h2\u003e \u003cp\u003eOur findings indicate a substantial LULC change in the study area over the past five years, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn order to quantify these changes, we exported the relevant data from the map to a table, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLULC classes changes in Km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClasses\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018–2019\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019–2020\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2020–2021\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2021–2022\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2022–2023\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2023–2024\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e129.27\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e142.16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e144.08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e141.26\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e131.05\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrees\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1528.72\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1463.14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1524.82\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1527.33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1490.88\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1443.84\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlooded vegetation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63.27\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60.31\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e64.79\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51.92\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrops\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1872.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1829.86\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1814.28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1798.40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1785.19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1625.22\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilt Area\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3067.26\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3153.28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3145.30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3182.69\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3198.87\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3379.03\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBare ground\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.78\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.53\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.59\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClouds\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.85\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.91\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.02\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRangeland\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e128.63\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e155.70\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97.74\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e100.49\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e158.72\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eBoth Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrated that built areas, water, rangeland and clouds have increased over the five years. In contrast, tree cover, flooded vegetation, and crops have decreased. Remarkably, the built-up area expanded prominently, reaching 180.16 Km\u003csup\u003e2\u003c/sup\u003e in 2023, a considerable rise compared to previous years, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThese findings suggest that the region has undergone rapid LULC alterations, impacting air quality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Air pollution\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. Sulphur dioxide\u003c/h2\u003e \u003cp\u003eSulphur dioxide (SO\u003csub\u003e2\u003c/sub\u003e) is a colourless gas with a pungent odour that becomes liquid under pressure and readily dissolves in water (National Library of Medicine \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It is a severe air pollutant that emitted from both anthropogenic and natural sources, primarily through biomass burning, which releases sulphur dioxide or sulphur oxides (SO\u003csub\u003ex\u003c/sub\u003e) depending on the composition of biomass (Khalaf et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; US EPA \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). Numerous researches have also shown that SO\u003csub\u003e2\u003c/sub\u003e has proved also it is discharged from activities such as open waste burning, incinerations, and agricultural biomass burning, especially during land preparation and other LULC changes leading to air pollution (Permadi and Kim Oanh \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Mallongi et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ahmad et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Firdaus et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAfter executing our GEE model for SO\u003csub\u003e2\u003c/sub\u003e and overlaying the results onto our region of interest, we found colour variations where black represents the lowest concentration level of SO\u003csub\u003e2\u003c/sub\u003e and cyan indicates the highest. From 2019 to 2023, with 2022 the year that SO2 pollution has impacted the most extensive area in our study area as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, these results were undertaking analysis with ArcGIS10.8; we found that its emissions were lower in 2019, with concentration ranging from a minimum of -0.0000623 to a maximum of 0.000187 mol/m\u003csup\u003e2\u003c/sup\u003e, while the increase in 2020 with a range of -0.000165 to 0.000222 mol/m\u003csup\u003e2\u003c/sup\u003e, raised in 2021 with a range of -0.000135 to 0.000281 mol/m\u003csup\u003e2\u003c/sup\u003e, dramatically surpassing levels of 2021 with a range of -0.000199 to 0.000319 mol/m\u003csup\u003e2\u003c/sup\u003e in 2022 and declined in 2023 with a range of – 0.0000441 to 0.000224 mol/m\u003csup\u003e2\u003c/sup\u003e. The significant increase in 2022 is related to reopening many activities locked during Covid-19 lockdowns. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e illustrates these findings, with red denoting higher emissions of SO\u003csub\u003e2\u003c/sub\u003e and black denoting lower emissions.\u003c/p\u003e \u003cp\u003eNotably, our time series results proved that 2023 had higher air pollution of SO\u003csub\u003e2\u003c/sub\u003e while 2020 recorded lower. Moreover, the highest value recorded over five years was 0.00037 mol/m\u003csup\u003e2\u003c/sup\u003e in 2022, while the lowest was − 0.00021 mol/m\u003csup\u003e2\u003c/sup\u003e in 2021. Furthermore, SO\u003csub\u003e2\u003c/sub\u003e emissions increased in May, June, July, and September over five years compared to the other months, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. This can be attributed to the summer season when people engage in much open burning, and there is no rain to cleanse the air.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2. Carbon monoxide\u003c/h2\u003e \u003cp\u003eCarbon monoxide (CO) is a highly poisonous, colourless, odourless, tasteless gas, and flammable in the air. It turns into liquid at lower temperatures and is insoluble in water above 70\u003csup\u003eo\u003c/sup\u003eC. CO is primarily formed through incomplete combustion from natural processes or anthropogenic activities (Wilbur et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Donald \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Waste releases CO through the decay of biomass (Stegenta-Dąbrowska et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and the open burning of solid waste or biomass (Ramadan et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Moreover, LULC changes contribute to CO emissions via deforestation, agricultural clearing, and urbanization, which reduce vegetation cover and alter landscapes. The findings that we got after running codes in GEE with the satellite range from 0 mol/m\u003csup\u003e2\u003c/sup\u003e (min) to 0.05 mol/m\u003csup\u003e2\u003c/sup\u003e (max). Yellow colour denotes the highest concentration, while cyan signifies the lowest. The data showed that 2019 had a higher concentration with more yellow colour than the subsequent years, predominated by green, as displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eAll the findings were then downloaded and uploaded into ArcGis10.8 to clarify CO emissions. The results revealed that CO concentration from 0.0285 to 0.0426 mol/m\u003csup\u003e2\u003c/sup\u003e in 2019, 0.0245 to 0.0354 mol/m\u003csup\u003e2\u003c/sup\u003e in 2020, 0.0231 to 0.0358 mol/m\u003csup\u003e2\u003c/sup\u003e in 2021, 0.0219 to 0.0341 mol/m\u003csup\u003e2\u003c/sup\u003e in 2022 and 0.0246 to 0.0405 mol/m\u003csup\u003e2\u003c/sup\u003e in 2023. These values were demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, with black indicating lower and red indicating higher concentrations.\u003c/p\u003e\u003cp\u003eFurthermore, the time series chart indicated that the highest emission recorded was 0.049 mol/m\u003csup\u003e2\u003c/sup\u003e in 2019 before Covid-19 and 0.048 mol/m\u003csup\u003e2\u003c/sup\u003e in 2023. Additionally, higher CO emissions were observed from August to October over the past five years, with a decrease starting in November, the second month of the rainy season, when rain begins to cleanse the air and reduce open burning, as revealed in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3. Formaldehyde\u003c/h2\u003e \u003cp\u003ehac Formaldehyde CH\u003csub\u003e2\u003c/sub\u003eO is colourless, pungent, highly flammable, irritating, and toxic gas with a low molecular weight in its pure form. It is soluble in water, alcohol, and other polar solvents. CH\u003csub\u003e2\u003c/sub\u003eO has strong electrophilic and reactive properties and the ability to polymerise, which can lead to the formation of explosive mixtures in the air. It can decompose into carbon monoxide and methanol at high temperatures and quickly oxidises to carbon dioxide in sunlight (Subasi \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). All these properties make it widely used in many applications. However, it is emitted into the atmosphere through the combustion of biomass, decomposition, open burning, and incineration (Kaden et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), as a result of waste disposal or LULC changes.\u003c/p\u003e \u003cp\u003eOur GEE model indicated a higher concentration of CH\u003csub\u003e2\u003c/sub\u003eO in 2019 and 2023, represented in red in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAfter that, these findings were exported in ArcGIS 10.8, and we found that the CH\u003csub\u003e2\u003c/sub\u003eO concentration levels ranged from 0.000144 to 0.000396 mol/m\u003csup\u003e2\u003c/sup\u003e in 2019, lowed to 0.0000877 to 0.000315 mol/m\u003csup\u003e2\u003c/sup\u003e in 2020, and further to 0.0000735 to 0.000313 mol/m\u003csup\u003e2\u003c/sup\u003e in 2021. Nevertheless, the levels increased again to 0.0000778 to 0.000338 mol/m\u003csup\u003e2\u003c/sup\u003e in 2022 and 0.000123 to 0.000397 mol/m\u003csup\u003e2\u003c/sup\u003e in 2023. Figure\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e represents these outcomes, with black stipulating lower values of CH\u003csub\u003e2\u003c/sub\u003eO emissions and red stipulating higher values of CH\u003csub\u003e2\u003c/sub\u003eO emissions.\u003c/p\u003e \u003cp\u003eMoreover, the time series chart results showed that the highest concentration levels were 0.00043 mol/m\u003csup\u003e2\u003c/sup\u003e in 2019 and 0.00038 mol/m\u003csup\u003e2\u003c/sup\u003e in 2023. Plus, a higher concertation of CH\u003csub\u003e2\u003c/sub\u003eO was observed from May to November for 2019 and 2023, while 2020, 2021, and 2022 did not rapidly increase due to Covid-19 lockdowns, which halted many businesses. The monthly CH\u003csub\u003e2\u003c/sub\u003eO emissions from 2019 to 2023 are displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.3.4. Nitrogen dioxide\u003c/h2\u003e \u003cp\u003eNitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e) is a highly reactive gas with a pungent odour and a reddish orange-brown colour. It acts as an oxidising agent, is corrosive, and non-combustible(Wang \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). NO\u003csub\u003e2\u003c/sub\u003e is emitted during combustion processes (Van Tran et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), from fireplaces, through the use of fertilizer inputs in agriculture, and from nitrogen-containing biomass decaying to release N\u003csub\u003e2\u003c/sub\u003eO (Ritchie et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which oxidises into NO\u003csub\u003e2\u003c/sub\u003e in ambient conditions when there is presence of oxidising agents like O\u003csub\u003e2\u003c/sub\u003e, O\u003csub\u003e3\u003c/sub\u003e and volatile organic compound (VOCs) (Jarvis et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur outcomes denoted that high NO\u003csub\u003e2\u003c/sub\u003e emission regions were detected in 2019, 2021, 2022 and 2023, spanning from black colour, indicative of the lowest concentration, to yellow, indicative of the highest, as demonstrated by the GEE findings, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTo further analyse NO\u003csub\u003e2\u003c/sub\u003e emission patterns, the newly acquired datasets were transferred to ArcGIS 10.8. The results revealed that NO\u003csub\u003e2\u003c/sub\u003e concentration levels ranged from 0.0000968 to 0.000175 mol/m\u003csup\u003e2\u003c/sup\u003e in 2019. These levels decreased to 0.0000974 to 0.000109 in 2020, rose again to 0.0000713 to 0.000169 mol/m\u003csup\u003e2\u003c/sup\u003e in 2021, varied between 0.0000385 to 0.000142 mol/m\u003csup\u003e2\u003c/sup\u003e in 2022 and increased to 0.000132 to 0.000166 mol/m\u003csup\u003e2\u003c/sup\u003e in 2023. These levels varied from black for lower emissions to red for higher emissions, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e16\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eMoreover, the time series analysis chart pointed out the highest NO2 concertation was 0.000135 mol/m\u003csup\u003e2\u003c/sup\u003e in 2019 and 0.000119 mol/m\u003csup\u003e2\u003c/sup\u003e in 2023. An increase was spotted from June to October in 2023. In the other years, higher concentration was noted in June, July, August and October, except for 2022, which signalled a decrease in October, as displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e17\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Impact of waste and LULC on air pollution\u003c/h2\u003e \u003cp\u003eOur research findings demonstrated that the increase in waste generation over five years resulted in decaying, open burning, landfills and incineration as solutions to manage the overwhelming waste burden. Additionally, changes in LULC have been marked by the expansion of built-up areas, rangeland, rose deforestation, reduced vegetation cover and other cropland. This decreased forest and vegetation cover has led to widespread biomass decomposition and burning. Consequently, these factors collectively have elevated the levels of pollutants such as CO, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e2\u003c/sub\u003eO levels in the ecosystem of Tangerang, Jakarta, Bogor, Bekasi and Depok over the past five years, as evidenced by increased emissions of these pollutants that we discussed in previous sections. Furthermore, cropland reduction has intensified crop production on limited land, necessitating the greater use of fertilizers and other chemicals to meet food demands. Consequently, these factors collectively have elevated the levels of pollutants such as CO, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e2\u003c/sub\u003eO levels in the ecosystem of Tangerang, Jakarta, Bogor, Bekasi and Depok over the past five years, as evidenced by increased emissions of these pollutants that we discussed in previous sections.\u003c/p\u003e \u003cp\u003eNonetheless, we have also noted a temporary decrease in these pollutants during Covid-19 lockdowns, underscoring the impact of reduced anthropogenic activities on air quality. Thus, the escalation of these air pollutants has had severe long-lasting effects, including respiratory diseases and other health issues. As a fact, recent studies have suggested staggering statistics, with over 7,000 health adversities in children, 10,0000 deaths, and more than 5,000 hospitalizations in Jakarta annually due to air pollution, alongside economic cost estimated at USD 2,943.42\u0026nbsp;million to mitigate these health issues each year (Syuhada et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, air pollution has exacerbated biotic stress, and contributed to climate change consequences such as El Niño, acid rain, increased temperature and sunshine, changes in windspeed and irregular rainfall (World Resources Institute \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAddressing air pollution remains a critical global challenge aggravated by anthropogenic activities, including waste and LULC changes that release CO, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e2\u003c/sub\u003eO pollutants through processes like open burning, incineration, organic decomposition and use of fertilizers. Our study has identified a pressing alteration in these driving factors from 2019 to 2023, contributing to rising air pollution levels of CO, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e2\u003c/sub\u003eO over this period, except 2020 and 2021 due to COVID-19 lockdowns. These findings underscore the influence of waste and LULC changes on air quality in the studied regions. For this reason, it is imperative to implement proactive measures to mitigate air pollution. We firmly advocate for a participatory, compressive approach to waste management and sustainable land management practices. These strategies include promoting the principles of reuse, recycle and reduce (3R), enforcing a ban on biomass and open burning; implementing reforestation, green gardening, and agroforestry; establishing modern green urban villages with multistorey buildings, and encouraging sustainable composting and use of organic manure as fertilizer to improve air quality and public health in Tangerang, Jakarta, Bogor, Bekasi and Depok.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e1.Oscar Umwanzisiwemuremyi: Conducted the research, analysed the data, and drafted the manuscript.2. Dr. Abidin Zaenal: Served as the primary supervisor, providing guidance, revisions, and final approval of the manuscript3. Dr. Yudi Setiawan: Acted as the co-supervisor, contributing guidance, revisions, and final approval of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003e\"All data on the Waste, Land cover and Land use changes, CO, H2CO, NO2 and SO2 that support the findings of this study are included within this paper: and as per below https://drive.google.com/drive/folders/1EZQ1Vr6xuuhXKHmr50pFJUNYcdnNRWpW?usp=sharing\"\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmad MN, Shao Z, Javed A. 2023. Modelling land use/land cover (LULC) change dynamics, future prospects, and its environmental impacts based on geospatial data models and remote sensing data. \u003cem\u003eEnviron Sci Pollut Res Int\u003c/em\u003e. 30(12):32985\u0026ndash;33001. doi:10.1007/S11356-022-24442-2.\u003c/li\u003e\n\u003cli\u003eANTARA. 2023. Govt highlights strategic role of waste banks in Indonesia. [accessed 2024 Jun. 1]. https://en.antaranews.com/news/284949/govt-highlights-strategic-role-of-waste-banks-in-indonesia.\u003c/li\u003e\n\u003cli\u003eBadan Informasi Geospasia. 2024. Tanah air indonesia (homeland). [accessed 2024 Feb. 27]. https://tanahair.indonesia.go.id/portal-web.\u003c/li\u003e\n\u003cli\u003eBadan Pusat Statistik. 2024. Number of Population by Regency/Municipality for Jawa Barat, Banten province and DKI Jakarta 2022 - 2024. [accessed 2024 May 24]. https://www.bps.go.id/id.\u003c/li\u003e\n\u003cli\u003eCrippa, M., Guizzardi, D., Pagani, F., Banja, M., Muntean, M., Schaaf E., Becker, W., Monforti-Ferrario, F. Q, R., Risquez Martin, A., Taghavi-Moharamli, P., K\u0026ouml;ykk\u0026auml;, J., Grassi, G., Rossi, S., Brandao De Melo, J., Oom, D., Branco, A., San-Miguel J, Vignati E. 2023. GHG emissions of all world countries. Publications Office of the European Union, Luxembourg.\u003c/li\u003e\n\u003cli\u003eDonald JA. 2015 Jan 1. Carbon Monoxide. \u003cem\u003eHandb Horm Comp Endocrinol Basic Clin Res\u003c/em\u003e., forthcoming.\u003c/li\u003e\n\u003cli\u003eESA. 2018a. Sentinel-5P OFFL CO: Offline Carbon Monoxide | Earth Engine Data Catalog | Google for Developers. [accessed 2024 May 29]. https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CO.\u003c/li\u003e\n\u003cli\u003eESA. 2018b. Sentinel-5P OFFL SO2: Offline Sulfur Dioxide | Earth Engine Data Catalog | Google for Developers. [accessed 2024 May 28]. https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_SO2.\u003c/li\u003e\n\u003cli\u003eESA. 2018c. Sentinel-5P OFFL NO2: Offline Nitrogen Dioxide | Earth Engine Data Catalog | Google for Developers. [accessed 2024 May 30]. https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_NO2.\u003c/li\u003e\n\u003cli\u003eESA. 2018d. Sentinel-5P OFFL HCHO: Offline Formaldehyde | Earth Engine Data Catalog | Google for Developers. [accessed 2024 May 26]. https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_HCHO.\u003c/li\u003e\n\u003cli\u003eESA. 2024a. Copernicus Sentinel-2 mission and application. [accessed 2024 May 27]. https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-2.\u003c/li\u003e\n\u003cli\u003eESA. 2024b. Sentinel-5P and its applications. [accessed 2024 May 27]. https://sentiwiki.copernicus.eu/web/sentinel-5p.\u003c/li\u003e\n\u003cli\u003eFirdaus FM, Elliott B, Malsch J, Surjadi P. 2023. 7 Things to Know About Jakarta\u0026rsquo;s Air Pollution Crisis. [accessed 2024 May 31]. https://wri-indonesia.org/en/insights/7-things-know-about-jakartas-air-pollution-crisis.\u003c/li\u003e\n\u003cli\u003eGe M, Friedrich J, Vigna L. 2020. 4 Charts Explain Greenhouse Gas Emissions by Countries and Sectors.\u003c/li\u003e\n\u003cli\u003eGEE. 2024a. Google Earth Education. [accessed 2024 May 29]. https://www.google.com/earth/education/tools/google-earth-engine/.\u003c/li\u003e\n\u003cli\u003eGEE. 2024b. Platform. [accessed 2024 May 29]. https://earthengine.google.com/platform/.\u003c/li\u003e\n\u003cli\u003eGEE. 2024c. Time Series Modeling for GEE for Developers. [accessed 2024 May 29]. https://developers.google.com/earth-engine/tutorials/community/time-series-modeling.\u003c/li\u003e\n\u003cli\u003eGhasempour F, Sekertekin A, Kutoglu SH. 2021. Google Earth Engine based spatio-temporal analysis of air pollutants before and during the first wave COVID-19 outbreak over Turkey via remote sensing. \u003cem\u003eJ Clean Prod\u003c/em\u003e. 319:128599\u0026ndash;128599. doi:10.1016/J.JCLEPRO.2021.128599.\u003c/li\u003e\n\u003cli\u003eGonzalez RC. 2018. Support Package for Digital Image Processing. [accessed 2024 May 30]. www.ImageProcessingPlace.com.\u003c/li\u003e\n\u003cli\u003eGorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R. 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. \u003cem\u003eRemote Sens Environ\u003c/em\u003e. 202:18\u0026ndash;27. doi:10.1016/J.RSE.2017.06.031.\u003c/li\u003e\n\u003cli\u003eIkram NM, Afifah L, Arthatia BS, Wicaksono SJ, Maharani M, Ediyanto, Ihsanudin T, Apriyanti D. 2022. Monitoring CO and O3concentration that caused climate change periodically using Google Earth Engine (study case: Java Island). \u003cem\u003eIOP Conf Ser Earth Environ Sci\u003c/em\u003e. 1047(1). doi:10.1088/1755-1315/1047/1/012021.\u003c/li\u003e\n\u003cli\u003eJarvis DJ, Adamkiewicz G, Heroux M-E, Rapp R, Kelly FJ. 2010. Nitrogen dioxide. [accessed 2024 Jun. 4]. https://www.ncbi.nlm.nih.gov/books/NBK138707/.\u003c/li\u003e\n\u003cli\u003eKaden DA, Mandin C, Nielsen GD, Wolkoff P. 2010. Formaldehyde. [accessed 2024 Jun. 3]. https://www.ncbi.nlm.nih.gov/books/NBK138711/.\u003c/li\u003e\n\u003cli\u003eKarra K, Kontgis C, Statman-Weil Z, Mazzariello JC, Mathis M, Brumby SP. 2021. GLOBAL LAND USE/LAND COVER WITH SENTINEL 2 AND DEEP LEARNING. \u003cem\u003eInt Geosci Remote Sens Symp\u003c/em\u003e. 2021-July:4704\u0026ndash;4707. doi:10.1109/IGARSS47720.2021.9553499.\u003c/li\u003e\n\u003cli\u003eKhalaf EM, Mohammadi MJ, Sulistiyani S, Ram\u0026iacute;rez-Coronel AA, Kiani F, Jalil AT, Almulla AF, Asban P, Farhadi M, Derikondi M. 2022 Dec 22. Effects of sulfur dioxide inhalation on human health: a review. \u003cem\u003eRev Environ Health\u003c/em\u003e., forthcoming.\u003c/li\u003e\n\u003cli\u003eKinakh V, Bun R, Danylo O. 2017. Geoinformation technology of analysis and vizualization of spatial data on greenhouse gas emissions using Google Earth Engine. \u003cem\u003eProc 12th Int Sci Tech Conf Comput Sci Inf Technol CSIT 2017\u003c/em\u003e. 1 September:212\u0026ndash;215. doi:10.1109/STC-CSIT.2017.8098771.\u003c/li\u003e\n\u003cli\u003eMallongi A, Safiu D, Amqam H, Syam A, Hatta M, Sutarti T, Abdul M, Siyoto S, Apollo A. 2019. Modelling of SO2and CO Pollution Due to Industry PLTD Emission Tello in Makassar Indonesia. \u003cem\u003eARPN J Eng Appl Sci\u003c/em\u003e. 14(2):634\u0026ndash;640. doi:10.36478/JEASCI.2019.634.640.\u003c/li\u003e\n\u003cli\u003eManisalidis I, Stavropoulou E, Stavropoulos A, Bezirtzoglou E. 2020. Environmental and Health Impacts of Air Pollution: A Review. \u003cem\u003eFront Public Heal\u003c/em\u003e. 8:14. doi:10.3389/FPUBH.2020.00014.\u003c/li\u003e\n\u003cli\u003eMinistry of Environment and Forestry. 2021. Indonesia Third Biennial Update Report. \u003cem\u003eRepub Indones\u003c/em\u003e., forthcoming.\u003c/li\u003e\n\u003cli\u003eNational Library of Medicine. 2024. Sulfur Dioxide compound. [accessed 2024 May 31]. https://pubchem.ncbi.nlm.nih.gov/compound/Sulfur-Dioxide#section=Entrez-Crosslinks.\u003c/li\u003e\n\u003cli\u003eOur World In Data. 2024. Community Emissions Data System\u0026quot;Emissions of Worldwide air pollutants CO,NOx,SO2 and non-CH4 VOCs 2019 to 2022. [accessed 2024 May 23]. https://ourworldindata.org/grapher/long-run-air-pollution?time=2019..latest.\u003c/li\u003e\n\u003cli\u003ePermadi DA, Kim Oanh NT. 2013. Assessment of biomass open burning emissions in Indonesia and potential climate forcing impact. \u003cem\u003eAtmos Environ\u003c/em\u003e. 78:250\u0026ndash;258. doi:10.1016/J.ATMOSENV.2012.10.016.\u003c/li\u003e\n\u003cli\u003ePopescu F, Ionel I, Popescu F, Ionel I. 2010 Aug 18. Anthropogenic Air Pollution Sources. \u003cem\u003eAir Qual\u003c/em\u003e., forthcoming.\u003c/li\u003e\n\u003cli\u003eRamadan BS, Rachman I, Matsumoto T. 2022. Activity and emission inventory of open waste burning at the household level in developing countries: a case study of Semarang City. \u003cem\u003eJ Mater Cycles Waste Manag\u003c/em\u003e. 24(3):1194. doi:10.1007/S10163-022-01371-3.\u003c/li\u003e\n\u003cli\u003eRitchie H. 2020. Sector by sector: where do global greenhouse gas emissions come from? - Our World in Data. \u003cem\u003eOurWorldInData.org\u003c/em\u003e., forthcoming. [accessed 2024 Jun. 4]. https://ourworldindata.org/ghg-emissions-by-sector#article-citation.\u003c/li\u003e\n\u003cli\u003eRitchie H, Rosado P, Roser M. 2024 Jan 5. Breakdown of carbon dioxide, methane and nitrous oxide emissions by sector. \u003cem\u003eOur World Data\u003c/em\u003e., forthcoming. [accessed 2024 Jun. 4]. https://ourworldindata.org/emissions-by-sector.\u003c/li\u003e\n\u003cli\u003eSIPSN. 2024. Waste corrected and disposed to TPA for 2019 - 2023. [accessed 2024 May 27]. https://sipsn.menlhk.go.id/sipsn/.\u003c/li\u003e\n\u003cli\u003eStegenta-Dąbrowska S, Drabczyński G, Sobieraj K, Koziel JA, Białowiec A. 2019. The Biotic and Abiotic Carbon Monoxide Formation During Aerobic Co-digestion of Dairy Cattle Manure With Green Waste and Sawdust. \u003cem\u003eFront Bioeng Biotechnol\u003c/em\u003e. 7:485662. doi:10.3389/FBIOE.2019.00283/BIBTEX.\u003c/li\u003e\n\u003cli\u003eSubasi NT. 2020 Jan 24. Formaldehyde Advantages and Disadvantages: Usage Areas and Harmful Effects on Human Beings. \u003cem\u003eBiochem Toxicol - Heavy Met Nanomater\u003c/em\u003e., forthcoming.\u003c/li\u003e\n\u003cli\u003eSyuhada G, Akbar A, Hardiawan D, Pun V, Darmawan A, Heryati SHA, Siregar AYM, Kusuma RR, Driejana R, Ingole V, \u003cem\u003eet al.\u003c/em\u003e 2023. Impacts of Air Pollution on Health and Cost of Illness in Jakarta, Indonesia. \u003cem\u003eInt J Environ Res Public Health\u003c/em\u003e. 20(4). doi:10.3390/IJERPH20042916/S1.\u003c/li\u003e\n\u003cli\u003eVan Tran V, Park D, Lee YC. 2020. Indoor Air Pollution, Related Human Diseases, and Recent Trends in the Control and Improvement of Indoor Air Quality. \u003cem\u003eInt J Environ Res Public Health\u003c/em\u003e. 17(8). doi:10.3390/IJERPH17082927.\u003c/li\u003e\n\u003cli\u003eUNEP. 2023. Emissions Gap Report 2023: Broken Record \u0026ndash; Temperatures hit new highs, yet world fails to cut emissions (again).\u003c/li\u003e\n\u003cli\u003eUS EPA. 2023a. Hazardous Air Pollutants: Sources and Exposure. [accessed 2024 May 23]. https://www.epa.gov/haps/hazardous-air-pollutants-sources-and-exposure.\u003c/li\u003e\n\u003cli\u003eUS EPA. 2023b. Overview of Sulfur Dioxide (SO2) Air Quality in the United States. https://www.epa.gov/system/files/documents/2022-08/SO2_2021.pdf.\u003c/li\u003e\n\u003cli\u003eWang NCY. 2024. Nitrogen dioxide (formerly nitrogen oxides). \u003cem\u003eEncycl Toxicol Fourth Ed Vol 1-9\u003c/em\u003e. 6:883\u0026ndash;889. doi:10.1016/B978-0-12-824315-2.00756-9.\u003c/li\u003e\n\u003cli\u003eWHO. 2021. \u003cem\u003eAir pollution. In: Compendium of WHO and other UN guidance on health and environment.\u003c/em\u003e World Health Organization. [accessed 2024 May 23]. https://cdn.who.int/media/docs/default-source/who-compendium-on-health-and-environment/who_compendium_chapter2_01092021.pdf?sfvrsn=14f84896_5.\u003c/li\u003e\n\u003cli\u003eWilbur S, Williams M, Williams R, Scinicariello F, Klotzbach JM, Diamond GL, Citra M. 2012. Toxicological Profile for Carbon Monoxide. \u003cem\u003eUS Agency Toxic Subst Dis Regist\u003c/em\u003e. June:1\u0026ndash;347. [accessed 2024 Jun. 3]. https://www.ncbi.nlm.nih.gov/books/NBK153693/.\u003c/li\u003e\n\u003cli\u003eWorld Resources Institute. 2023. Southeast Asian Cities Have Some of the Most Polluted Air in the World. El Ni\u0026ntilde;o Is Making it Worse. [accessed 2024 Jun. 5]. https://www.wri.org/insights/air-pollution-southeast-asia-cities-jakarta-el-nino.\u003c/li\u003e\n\u003cli\u003eYancho JMM, Jones TG, Gandhi SR, Ferster C, Lin A, Glass L. 2020. The google earth engine mangrove mapping methodology (Geemmm). \u003cem\u003eRemote Sens\u003c/em\u003e. 12(22):1\u0026ndash;35. doi:10.3390/rs12223758.\u003c/li\u003e\n\u003cli\u003eYUE XL, GAO QX. 2018. Contributions of natural systems and human activity to greenhouse gas emissions. \u003cem\u003eAdv Clim Chang Res\u003c/em\u003e. 9(4):243\u0026ndash;252. doi:10.1016/j.accre.2018.12.003.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Air pollution, Google Earth Engine (GEE), Land Use / Land Cover (LULC), Sentinel 2, Sentinel-5 Precursor, waste","lastPublishedDoi":"10.21203/rs.3.rs-4932823/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4932823/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn recent years, air pollution has intensified due to a continuous rise in atmospheric pollutants. Emissions from anthropogenic activities have exacerbated this problem, contributing to health, environmental, and climate issues. Researchers have identified energy production, industrial processes, wildfires, and volcanic eruption as leading contributors, with waste, changes in land use and land cover (LULC) being secondary factors. Consequently, the growing global waste production and alterations in LULC are among factors in increasing pollution levels, which we seek to emphasise in this research. We specifically investigated the effects of waste and LULC on carbon monoxide (CO), Sulphur dioxide SO\u003csub\u003e2\u003c/sub\u003e, nitrous dioxide (NO\u003csub\u003e2\u003c/sub\u003e), and formaldehyde (CH\u003csub\u003e2\u003c/sub\u003eO) emissions in the Jakarta Metropolitan region, Indonesia. Data were gathered from the Sentinel 2 and Sentinel-5 Precursor satellites and National Waste Management Information System Indonesia (SIPSN) from 2019 to 2023, we employed excel, image analysis, and Google Earth Engine (GEE) for data processing. ArcGIS 10.8 was used for advanced mapping. Our findings revealed that CO, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and CH\u003csub\u003e2\u003c/sub\u003eO levels have increased the past five years in correlation with waste and LULC change, with a dip in 2020 during the COVID-19 lockdowns. Efforts to mitigate air pollution include curbing these emissions and promoting green urban planning.\u003c/p\u003e","manuscriptTitle":"Investigating Waste and Land Use Changes Effects on Air Pollution Using Google Earth Engine in Jakarta Metropolitan in Indonesia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-18 08:48:29","doi":"10.21203/rs.3.rs-4932823/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9bd5bc4f-5713-4adf-8946-bcfd1bdc9024","owner":[],"postedDate":"October 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":38180801,"name":"Earth and environmental sciences/Climate sciences"},{"id":38180802,"name":"Earth and environmental sciences/Environmental sciences"}],"tags":[],"updatedAt":"2025-01-06T04:23:19+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-18 08:48:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4932823","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4932823","identity":"rs-4932823","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}