Indian Institute of Tropical Meteorology, Pune, India.
Central University of Jharkhand, Ranchi, India.
J Environ Manage. 2023 Feb 15;328:116907. doi: 10.1016/j.jenvman.2022.116907. Epub 2022 Dec 6.
Lockdowns enforced amid the pandemic facilitated the evaluation of the impact of emission reductions on air quality and the production regime of O under NOx reduction. Analysis of space-time variation of various pollutants (PM10, PM2.5, NOx, CO, O and VOC or TNMHC) through the lockdown phases at eight typical stations (Urban/Metro, Rural/high vegetation and coastal) is carried out. It reveals how the major pollutant (PM10 or PM2.5 or O, or CO) differs from station to station as lockdowns progress depending on geography, land-use pattern and efficacy of lockdown implementation. Among the stations analyzed, Delhi (Chandnichowk), the most polluted (PM10 = 203 μgm; O = 17.4 ppbv) in pre-lockdown, experienced maximum reduction during the first phase of lockdown in PM2.5 (-47%), NO (-40%), CO (-37%) while O remained almost the same (2% reduction) to pre-lockdown levels. The least polluted Mahabaleshwar (PM10 = 45 μgm; O = 54 ppbv) witnessed relatively less reduction in PM2.5 (-2.9%), NO (-4.7%), CO (-49%) while O increased by 36% to pre-lockdown levels. In rural stations with lots of greenery, O is the major pollutant attributed to biogenic VOC emissions from vegetation besides lower NO levels. In other stations, PM2.5 or PM10 is the primary pollutant. At Chennai, Jabalpur, Mahabaleshwar and Goa, the deciding factor of Air Quality Index (AQI) remained unchanged, with reduced values. Particulate matter, PM10 decided AQI for three stations (dust as control component), and PM2.5 decided the same for two but within acceptable limits for stations. Improvement of AQI through control of dust would prove beneficial for Chennai and Patiala; anthropogenic emission control would work for Chandani chowk, Goa and Patiala; emission control of CO is required for Mahabaleshwar and Thiruvanathapuram. Under low VOC/NOx ratio conditions, O varies with the ratio, NO/NO, with a negative (positive) slope indicating VOC-sensitive (NOx-sensitive) regime. Peak O isopleths as a function of NOx and VOC depicting distinct patterns suggest that O variation is entirely non-linear for a given NOx or VOC.
在大流行期间实施的封锁措施促进了评估减排对空气质量和 NOx 减排下 O 产生机制的影响。通过在八个典型站点（城市/地铁、农村/高植被和沿海）的封锁阶段分析各种污染物（PM10、PM2.5、NOx、CO、O 和 VOC 或 TNMHC）的时空变化。它揭示了随着封锁的进展，主要污染物（PM10 或 PM2.5 或 O 或 CO）如何因地理位置、土地利用模式和封锁实施效果的不同而在每个站点有所不同。在所分析的站点中，德里（钱德尼乔克）在封锁前污染最严重（PM10=203μg/m3；O=17.4ppbv），在封锁的第一阶段 PM2.5 减少最多（-47%），NO（-40%），CO（-37%），而 O 几乎保持不变（减少 2%）到封锁前的水平。污染最轻的马哈拉施特拉邦（PM10=45μg/m3；O=54ppbv）的 PM2.5 减少相对较少（-2.9%），NO（-4.7%），CO（-49%），而 O 增加了 36%，达到了封锁前的水平。在植被茂盛的农村地区，O 是主要污染物，除了较低的 NO 水平外，它还归因于植物的生物源 VOC 排放。在其他站点，PM2.5 或 PM10 是主要污染物。在钦奈、贾巴尔普尔、马哈拉施特拉邦和果阿，空气质量指数（AQI）的决定因素保持不变，数值降低。颗粒物 PM10 决定了三个站点的 AQI（尘埃作为控制成分），PM2.5 决定了两个站点的 AQI，但在站点内是可接受的。通过控制尘埃来改善 AQI 将对钦奈和帕蒂亚拉有益；人为排放控制将对钱德尼乔克、果阿和帕蒂亚拉有效；需要控制马哈拉施特拉邦和特里凡得琅的 CO 排放。在低 VOC/NOx 比条件下，O 随比（NO/NO）而变化，负（正）斜率表示 VOC 敏感（NOx 敏感）机制。O 等高线作为 NOx 和 VOC 的函数的峰值描绘了明显的模式，表明对于给定的 NOx 或 VOC，O 的变化是完全非线性的。
