Asosiy kontentga oʻtish
Maqola

Urban Noise Pollution and Public Health in Samarkand: A Spatial and Statistical Assessment for Sustainable Urban Development

Ashurmakhmatov Sarvar Isroil UgliDepartment of Ecology and Life Safety, Samarkand State University Named After Sharaf Rashidov, Samarkand 140104, UzbekistanNilufar KomilovaDepartment of Ecology, National University of Uzbekistan Named After Mirzo Ulugbek, Tashkent 100174, UzbekistanDilnoza ZaynutdinovaDepartment of Socio-Economic Geography, Sharof Rashidov Samarkand State University, Samarkand 140104, UzbekistanIsabek B. MurtazaevDepartment of Economic Theory, National University of Uzbekistan Named After Mirzo Ulugbek, Tashkent 100174, UzbekistanBakhodir MakhmudovDepartment of Geography and Fundamentals of Economic Knowledge, National Pedagogical University of Uzbekistan Named After Nizami, Tashkent 100156, UzbekistanHusniddin EgamqulovDepartment of Ecology and Geography, Gulistan State University, Gulistan 120100, UzbekistanAigul SergeyevaDepartment of Physical and Economical Geography, L.N. Gumilyov Eurasian National University, Astana 010000, KazakhstanRoza IzimovaDepartment of Biology, K. Zhubanov Aktobe Regional University, Aktobe 030000, Kazakhstan
2026en
Ochiq kirishOltin

Litsenziya: cc-by

ABI

Annotatsiya

Environmental noise is a major environmental and public health concern in rapidly urbanizing cities, yet integrated studies combining field measurements, GIS-based spatial analysis, and predictive modelling remain limited in Central Asia. This study assessed the spatial distribution of urban noise pollution in Samarkand (Uzbekistan) and explored its statistical association with selected public health indicators, forecasting future trends. Measurements were conducted at 50 georeferenced sites covering more than 300 streets. The measured data were processed and mapped using ArcGIS 10.5 (Esri, Redlands, CA, USA) to produce the spatial distribution of environmental noise across the study area. Official data on registered vehicles, industrial enterprises, and disease incidence (2014–2024) were analysed using Pearson correlation, Autoregressive Integrated Moving Average (ARIMA), and its extension incorporating exogenous variables (ARIMAX) models. Results revealed pronounced spatial heterogeneity in noise levels, highest along transport corridors and industrial zones. Industrial enterprises showed the strongest correlations with disease incidence; vehicle registrations were excluded from final models owing to collinearity with industrial activity. ARIMA projected continued industrial growth through 2030, while ARIMAX models identified significant associations between industrial activity and diseases of the ear and mastoid process and of the nervous system (MAPE 28.55% and 19.78%). As an ecological, exploratory study using infrastructural proxies rather than measured noise exposure, findings should be interpreted as associations rather than causation. The framework offers a transferable approach for environmental risk assessment and sustainable urban planning.

Hali tarjima qilinmagan

Identifikatorlar

Iqtiboslar va manbalar