Urban green areas are a potential supplier of air quality regulation service. However, research to date has mostly focused on the effects of the amount of these areas, with few studies evaluating how configuration aspects, such as spatial fragmentation, affect air quality services. Even less is known about how this service varies with decreasing pollutant emissions. Here we fill these research gaps by testing the contribution of green areas composition and configuration in reducing air pollution, before and during the COVID-19 quarantine period, in the largest city of the Global South (São Paulo, Brazil). We relied on a model selection approach using hourly concentrations of different pollutants (CO, NO2, PM2.5, and PM10,) as response variables. As predictors, we consider meteorological variables, the amount and fragmentation of green areas (related to air quality regulation supply), the quantity of vehicle emissions (proxy of demand pressure), all this at different spatial scales (proxy of pollutant flows from emission to supply areas). Our results showed that higher tree cover and lower vehicular emissions decreased concentrations of CO, NO2 and PM. Air quality regulation was higher in periods of low demand (start of quarantine), when compared to periods of high demand (before and the last part of quarantine). Lower levels of pollutants were associated with greater amounts of green areas at scales of up to 1,000 m from the air quality monitoring station. This indicates that the presence of green areas can have positive effects on air quality at distances of up to 1,000 m from the sites where pollutants are emitted. Our results show that to enhance air regulation services in large urban areas, it is important to maximize the amount of green areas and minimize their fragmentation, beyond the reduction of vehicular emissions.

中文翻译：

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空气调节服务受绿地覆盖和碎片化的影响：使用 COVID-19 隔离期间的需求、供应和流量的分析


城市绿地是空气质量监管服务的潜在供应商。然而，迄今为止的研究主要集中在这些区域数量的影响上，很少有研究评估配置方面（例如空间碎片化）如何影响空气质量服务。关于这项服务如何随着污染物排放的减少而变化，人们知之甚少。在这里，我们通过测试全球南方最大城市（巴西圣保罗）在 COVID-19 隔离期之前和期间绿地组成和配置对减少空气污染的贡献来填补这些研究空白。我们依赖于一种模型选择方法，使用不同污染物 （CO、NO2、PM2.5 和 PM10） 的每小时浓度作为响应变量。作为预测因子，我们考虑了气象变量、绿地的数量和碎片化（与空气质量调节供应有关）、车辆排放量（需求压力的代理），所有这些都在不同的空间尺度上（从排放到供应区域的污染物流的代理）。我们的结果表明，较高的树木覆盖率和较低的车辆排放降低了 CO、NO2 和 PM 的浓度。与高需求时期（隔离前和最后阶段）相比，低需求时期（隔离开始）的空气质量监管更高。在距离空气质量监测站高达 1,000 m 的范围内，污染物水平较低与绿地数量较多有关。这表明，绿地的存在会对距离污染物排放地点最远 1,000 m 的空气质量产生积极影响。 我们的结果表明，为了加强大城市地区的空气调节服务，除了减少车辆排放之外，重要的是最大限度地提高绿地的数量并最大限度地减少其碎片化。