Pengaruh Polusi Udara terhadap Deposisi Asam di Daerah Sekitar Pantai
Bibliographic record
Abstract
Acid deposition resulting from air pollution has various negative impacts and has emerged as a global issue, leading to human health problems. The primary causes of acid deposition are pollutants originating from both natural and anthropogenic sources. This study aimed to identify the chemical composition characteristics related to acid deposition in coastal areas, encompassing both wet and dry deposition. Sampling took place in Pameungpeuk, Garut Regency, from 2012 to 2018. The wet deposition was monitored by collecting rainwater samples using rain gauges, while dry deposition was assessed through passive sampler methods in ambient air. Laboratory analysis revealed that Na+ and Cl- ions were highly dominant in rainwater, with average concentrations of 200.70 µmol/L and 181.29 µmol/L, respectively. Regarding ambient air measurements, the average concentration of O3 was the highest among other parameters, at 18.88 µg/m3. The concentrations of dissolved ions and ambient air are significantly influenced by rainfall, acting as a diluent in the atmosphere. ABSTRAK Deposisi asam sebagai akibat dari polusi udara memberikan dampak negatif yang luas dan telah menjadi permasalahan global dan menyebabkan gangguan kesehatan manusia. Penyebab utama deposisi asam adalah polutan yang dihasilkan dari sumber alami dan juga antropogenik. Penelitian ini dilakukan untuk mengetahui karakteristik komposisi kimia dalam kaitannya dengan deposisi asam di daerah pantai baik secara basah maupun kering. Sampling dilakukan di Pameungpeuk, Kabupaten Garut dari tahun 2012-2018. Pemantauan deposisi basah dengan melakukan pengukuran pada sampel air hujan yang ditampung dengan penakar air hujan, sedangkan deposisi kering dengan menggunakan metode passive sampler pada udara ambien. Dari hasil analisis di laboratorium menunjukkan bahwa ion Na+ dan Cl- keberadaannya sangat dominan pada air hujan, dengan konsentrasi rata-rata ion Cl- sebesar 200,70 µmol/L dan ion Na+ 181,29 µmol/L. Hasil pengukuran udara ambien, konsentrasi rata-rata O3 paling tinggi di antara parameter lainnya yaitu sebesar 18,88 µg/m3. Tinggi rendahnya konsentrasi dari ion terlarut dan udara ambien sangat dipengaruhi oleh curah hujan yang berperan sebagai pengencer di atmosfer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".