ABSTRACT

Air quality is a global challenge issue, and many regions of the world, such as India, are experiencing daunting challenges. An important aspect is to identify and then control the emissions from major contributing sources. To advance this aspect, this paper describes an air quality network that has been set up in the National Capital Territory of Delhi (NCT-Delhi) to identify major contributing source categories in real-time. The various components include an innovative cloud-based dashboard to compile the data in real-time from a series of PM instruments (Beta Attenuation Monitors (BAM)) and a low-cost sensor network (22 APT- MAXIMA sensors). Furthermore, at one of the locations (urban site), three real-time chemical speciation monitors are installed to provide elemental speciation (30 elements), elemental carbon (EC), and organic carbon (OC) data. PM_2.5 concentrations at different sites (urban, industrial, and background) were compared to the BAM measurements over an 8-month period from May 2019 to February 2020; spanning the summer, monsoon, autumn, and winter seasons in Delhi. The APT sensor measurements were well correlated to the BAM measurements, with R² values ranging between 0.84 and 0.95 for all sites. This validated that the APT-MAXIMA low-cost sensors can be a useful tool for distributed monitoring of PM_2.5 levels. The mean PM_2.5 concentrations showed a trend with winter (Dec, Jan, Feb: 205.2 ± 95.1 µg m⁻³) and autumn (Oct, Nov: 171.7 ± 128.3 µg m⁻³) highs and summer (May, Jun: 64.6 ± 57.2 µg m⁻³) and monsoon (Jul, Aug, Sep: 27.6 ± 16.7 µg m⁻³) lows. The bivariate polar plot reveals high PM_2.5 levels originated from local/regional combustion sources located east and south-west of the urban site, especially when high PM_2.5 episodes are encountered during the festival season and other smog episodes.

Implications: Low-cost sensors are useful for distributed monitoring under both low and high pollution conditions. A cloud-based dashboard system provided real-time, remote access to the data and in the visualization of air quality in the entire region. The real-time data availability on the cloud enabled establishing hot-spot regions of air pollution, spatial variation of PM_2.5, real-time source apportionment, and health risk estimates to benefit both policy makers, and the general public.

摘要

空气质量是一个全球性的挑战问题，世界上许多地区，例如印度，都面临着艰巨的挑战。一个重要方面是识别并控制主要排放源的排放。为了推进这方面的工作，本文描述了在德里国家首都辖区 (NCT-Delhi) 建立的空气质量网络，以实时识别主要的贡献源类别。各种组件包括一个创新的基于云的仪表板，用于实时编译来自一系列 PM 仪器（Beta 衰减监测器 (BAM)）和一个低成本传感器网络（22 个 APT-MAXIMA 传感器）的数据。此外，在其中一个地点（城市站点），安装了三个实时化学形态监测器，以提供元素形态（30 个元素）、元素碳 (EC)、和有机碳 (OC) 数据。下午在 2019 年 5 月至 2020 年 2 月的 8 个月期间，将不同地点（城市、工业和背景）的_2.5浓度与 BAM 测量值进行了比较；跨越德里的夏季、季风、秋季和冬季。APT 传感器测量值与 BAM 测量值密切相关，所有站点的R ²值范围在 0.84 和 0.95 之间。这证实了 APT-MAXIMA 低成本传感器可以成为分布式监测 PM _2.5水平的有用工具。PM _2.5平均浓度呈现出随着冬季（12 月、1 月、2 月：205.2 ± 95.1 µg m ^-3）和秋季（10 月、11 月：171.7 ± 128.3 µg m ^-3）和夏季（5 月、6 月：64.6 ± 57.2 微克·m ^-3) 和季风 (7 月、8 月、9 月：27.6 ± 16.7 µg m ^-3 ) 低点。双变量极坐标图显示高 PM _2.5水平源自位于城市场地东部和西南部的局部/区域燃烧源，尤其是在节日期间遇到高 PM _2.5事件和其他烟雾事件时。

影响：低成本传感器可用于在低污染和高污染条件下进行分布式监测。基于云的仪表板系统提供了对数据的实时远程访问以及整个地区空气质量的可视化。云上的实时数据可用性能够建立空气污染热点区域、PM _{2.5 的}空间变化、实时源分配和健康风险估计，以使政策制定者和公众受益。