Pengujian Hipotesis Pengaruh Penggunaan Lahan terhadap Kualitas Udara
Bibliographic record
Abstract
Abstract. Air is an important factor in life, but its quality has changed due to the physical development of cities such as industrial centres and settlements, so that the air that was initially clean is now dirty due to pollution from human activities such as transportation and industry According to the United States Environmental Protection Agency, air pollution contains harmful substances such as particulates, gases, vapours, dust, and smoke coming from vehicles, factories, and other human activities The city of Bandung consists of 8 sub-regions, and this study focuses on SWK Bojonegara because of its diverse land uses and proximity to the city centre, which leads to high mobilisation and pollutionKeywords: Land use, air quality, pollutant parameters. Deforesta tion and urbanisation in the region reduce natural carbon sequestration, whilst Bandung's mountainous morphology exacerbates air pollution containment Air quality monitoring is important for pollution control, and the test results are compared to ambient air quality standards In this study, the bivariate method was used to explore the relationship between land use and air quality, with quantitative analysis comparing pollutant parameters and qualitative analysis through interviews with related agencies The air quality index method is used to determine the condition of pollutants, regulated in Government Regulation number 41 of 1999, and multiple linear regression analysis shows the influence of land use on air quality, with results varying from strong (Codan NO2) to weak (TSP, PM25). Therefore, the development of stricter environmental policies and support for clean technology is needed to improve air quality and land use management in the city of Bandung Abstrak. Udara merupakan faktor penting dalam kehidupan, namun kualitasnya mengalami perubahan akibat perkembangan fisik kota seperti pusat industri dan permukiman, sehingga udara yang awalnya bersih kini menjadi kotor akibat pencemaran dari aktivitas manusia seperti transportasi dan industri. Menurut United States Environmental Protection Agency, polusi udara mengandung zat berbahaya seperti partikulat, gas, uap, debu, dan asap yang berasal dari kendaraan, pabrik, dan aktivitas manusia lainnya. Kota Bandung terdiri dari 8 sub-wilayah, dan penelitian ini berfokus pada SWK Bojonegara karena beragam penggunaan lahannya dan kedekatannya dengan pusat kota, yang menyebabkan tingginya mobilisasi dan polusi. Deforestasi dan urbanisasi di kawasan ini mengurangi penyerapan karbon alami, sementara morfologi Bandung yang dikelilingi pegunungan memperburuk penahanan polusi udara. Pemantauan kualitas udara penting untuk pengendalian pencemaran, dan hasil pengujian dibandingkan dengan baku mutu udara ambien. Dalam penelitian ini, metode bivariat digunakan untuk mengeksplorasi pengaruh antara penggunaan lahan dan kualitas udara, dengan analisis kuantitatif membandingkan parameter polutan dan analisis kualitatif melalui wawancara dengan instansi terkait. Metode indeks kualitas udara digunakan untuk mengetahui kondisi polutan, diatur dalam Peraturan Pemerintah nomor 41 tahun 1999, dan analisis regresi linier berganda menunjukkan pengaruh penggunaan lahan terhadap kualitas udara, dengan hasil yang bervariasi dari pengaruh kuat (CO dan NO2) hingga lemah (TSP, PM2.5). Oleh karena itu, pengembangan kebijakan lingkungan yang lebih ketat dan dukungan teknologi bersih diperlukan untuk meningkatkan kualitas udara dan pengelolaan penggunaan lahan di Kota Bandung.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".