ATMOSFERA HAVOSI MONITORINGIDA IOT SENSORLARI O‘LCHASH NATIJALARINING METROLOGIK ISHONCHLILIGINI BAHOLASH VA OSHIRISH METODOLOGIYASINI TAKOMILLASHTIRISH
DOI:
https://doi.org/10.5281/zenodo.21262890Ключевые слова:
atmosfera havosi sifati, IoT sensorlari, raqamli monitoring, metrologik ishonchlilik, o‘lchash noaniqligi, kalibrlash, adaptiv kalibrlash, havo ifloslanishi, PM2.5, PM10, sensor drifti, ma’lumotlarni qayta ishlash, ekologik monitoring, metrologik ta’minot, standartlashtirish.Аннотация
Atmosfera havosining ifloslanishi inson salomatligi, ekologik xavfsizlik va barqaror rivojlanishga salbiy ta’sir
ko‘rsatuvchi asosiy omillardan biri hisoblanadi. So‘nggi yillarda havo sifati monitoringida IoT texnologiyalariga asoslangan
raqamli sensor tizimlaridan keng foydalanilmoqda. Biroq bunday sensorlarning o‘lchash aniqligi, kalibrlash sifati, tashqi
muhit omillariga sezgirligi hamda uzoq muddatli barqarorligi bilan bog‘liq muammolar monitoring natijalarining metrologik
ishonchliligini pasaytirmoqda. Ushbu maqolada atmosfera havosi monitoringida qo‘llaniladigan IoT sensorlari o‘lchash
natijalarining metrologik ishonchliligini baholash va oshirish masalalari tadqiq etilgan. Tadqiqotda sensor ma’lumotlarining
aniqligiga ta’sir etuvchi omillar tahlil qilinib, o‘lchash noaniqligini baholash, adaptiv kalibrlash va ma’lumotlarni qayta ishlash
usullariga asoslangan takomillashtirilgan metodologik yondashuv ishlab chiqilgan. Taklif etilgan metodologiya sensor
ma’lumotlarining aniqligi va takrorlanuvchanligini oshirish, monitoring natijalarining ishonchliligini ta’minlash hamda
atmosfera havosi sifatini baholash va boshqarish samaradorligini yaxshilash imkonini beradi. Tadqiqot natijalari raqamli
monitoring tizimlarining metrologik ta’minotini rivojlantirish va ekologik monitoring amaliyotini takomillashtirishga xizmat
qiladi.
Библиографические ссылки
Alsamrai O., Al-Bayati A., Al-Quraan M. A Systematic Review for Indoor and Outdoor Air Pollution Monitoring Systems
Based on Internet of Things. Sustainability. 2024. Vol. 16(11). Article 4353.
Gololo M.G.D., et al. Review of IoT Systems for Air Quality Measurements Based on LTE/4G and LoRa Communications.
Smart Cities. 2024. Vol. 5(4). Article 32.
Garcia A., et al. Advancements in Air Quality Monitoring: A Systematic Review of IoT and AI-Based Systems. Artificial
Intelligence Review. 2025.
Hayward I., et al. Low-Cost Air Quality Sensors: Biases, Corrections and Challenges in Their Comparability.
Atmosphere. 2024. Vol. 15(12). Article 1523.
Tariq H., Touati F., Crescini D., Mnaouer A.B. State-of-the-Art Low-Cost Air Quality Sensors, Assemblies, Calibration
and Evaluation: A Systematic Review. Atmosphere. 2024. Vol. 15(4). Article 471.
Kim Y.H., et al. Machine Learning-Based Quality Control for Low-Cost Air Quality Sensor Networks: A Review.
Atmosphere. 2025. Vol. 16(10). Article 1136.
Taştan M., et al. Machine Learning–Based Calibration and Performance Evaluation of Low-Cost PM Sensors. Sensors.
Vol. 25(10). Article 3183.
Han P., et al. Calibrations of Low-Cost Air Pollution Monitoring Sensors: Model Development and Sensor Performance
Evaluation. Sensors. 2021. Vol. 21(1). Article 256.
Chacón-Mateos M., et al. Calibration and Performance Evaluation of PM2.5 and NO2 Air Quality Sensors. Atmospheric
Measurement Techniques. 2025. Vol. 18. pp. 4061–4085.
Hayward I., et al. The Impact of Device Type and Deployment Environment on Low-Cost Air Quality Sensor Performance.
npj Clean Air. 2025.
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