Present paper represents the spatio-temporal variation of air quality and performances of geostatistical tools for identification of pollutants zone in various district of Assam (India). Geographic Information System (GIS) and geostatistical analysis were utilized to estimate the spatio-temporal variations (2015-2017) of gaseous and particulate air pollutants. Data of twenty-three fixed monitoring stations were collected from the Central Pollution Control Board (CPCB). It was observed that SO₂ and NO_x concentrations are the major pollutants to the deterioration of air quality in Assam State. Exploratory data analysis was considered for determination of spatial and temporal patterns of air pollutants. Air Quality index (AQI) was calculated based on the air pollutants and particulate matter. Radial Basis Function (RBF) interpolation techniques was used to analyze the spatial and temporal variation of air quality in Assam. Cross-validation is applied to evaluate the accuracy of interpolation methods in terms of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Nash-Sutcliffe Equation (NSE) and Accuracy Factor (ACFT). In 2015, the high value of AQI portrayed in the central and northeast of the state. In 2016, the central and entire east of the study area was recorded the highest value of AQI. In 2017, it was observed that mostly the central part of the state recorded the high value of AQI. The spatio-temporal variation trend of air pollutants provides sound scientific basis for its management and control. This information of air pollution congregation would be valuable for urban planners and decision architects to efficiently administer air quality for health and environmental purposes.

Implication Statement

Guwahati is one of the most polluted cities in India provided a novel evidence to find out the impact of air pollution. Present study has been suffered from several limitations, like (i) the daily or weekly concentration of air pollutants were not gained due to limited monitoring technique, (2) dearth of regular information of PM2.5 collection, which were not regularly connected. Present study is used to estimate the spatio-temporal variations (2015-2017) of gaseous and particulate air pollutants using GIS and spatial statistical approach. Probably, this is the first study to report the spatial and temporal variation of air quality distribution in Assam. Results showed there is a negative impact on the ambient air quality status of Assam. These industries and mining areas contribute significantly to the air pollution in this deltaic region. This district-wise information of air pollution congregation would be valuable for urban planners and decision architects to efficiently administer air quality for health and environmental purposes. The dissimilarity in geographical dissemination of the pollutant concentration has been more helpful in seasonal inevitability. Consequently, a continuous set of data and more parameters can be included to attain more reliable results.

本文介绍了空气质量的时空变化和用于确定阿萨姆邦（印度）各个地区污染物区域的地统计学工具的性能。利用地理信息系统（GIS）和地统计分析来估计气态和颗粒状空气污染物的时空变化（2015-2017）。从中央污染控制委员会（CPCB）收集了23个固定监测站的数据。观察到SO ₂和NO _x浓度是导致阿萨姆邦空气质量恶化的主要污染物。为了确定空气污染物的时空格局，曾考虑采用探索性数据分析方法。根据空气污染物和颗粒物计算出空气质量指数（AQI）。径向基函数（RBF）插值技术用于分析阿萨姆邦空气质量的时空变化。交叉验证可根据均方根误差（RMSE），平均绝对百分比误差（MAPE），纳什-苏克利夫方程（NSE）和精度因子（ACFT）评估插值方法的准确性。2015年，AQI的高价值体现在该州的中部和东北部。在2016年，研究区域的中部和整个东部地区的AQI值最高。在2017年，据观察，该州的中部大部分地区记录了较高的AQI值。大气污染物的时空变化趋势为其管理和控制提供了良好的科学依据。空气污染聚集的这些信息对于城市规划者和决策者为健康和环境目的有效管理空气质量将是有价值的。

暗示声明

古瓦哈提（Guwahati）是印度污染最严重的城市之一，为发现空气污染的影响提供了新颖的证据。当前的研究受到一些限制，例如：（i）由于监测技术有限，无法获得每日或每周的空气污染物浓度；（2）缺乏定期收集的PM2.5收集信息。本研究用于使用GIS和空间统计方法估算气态和颗粒状空气污染物的时空变化（2015-2017）。可能这是第一个报告阿萨姆邦空气质量分布的时空变化的研究。结果表明，这对阿萨姆邦的空气质量状况有负面影响。这些工业和矿区大大增加了该三角洲地区的空气污染。这种区域性的空气污染聚集信息对于城市规划人员和决策者为健康和环境目的有效管理空气质量将非常有用。污染物浓度在地理分布上的差异性在季节性必然性方面更为有用。因此，可以包括一组连续的数据和更多参数以获得更可靠的结果。