Nowadays, increasing particulate matter (PM) remains a challenge for environmental and humanity health and increases death statistics. Case studies and experimental observations demonstrated that vegetation coverage and type of plants affect PM and air quality. Condensation of PM2.5 has an impressive effect on deteriorating air quality. Increasing vegetation coverage has a significant impact on reducing PM2.5. However, the requirement percent vegetation cover (PVC) is likewise a shadow for careful analysis to recommend the requirement percent and area of vegetation for different parts of the metropolitan. In this paper, we propose a four-phase intelligent algorithm for investigating PM2.5 and critical situations to detect unhealthy air quality monitoring stations (AQMSs). Our algorithm makes a decision based on fuzzy and neural network methods and recommends the percent density and area of vegetation. Our analysis of the weather condition is event-driven, considering rainfall as an event to examine the situation of each AQMS before and after rainfall. The experiments demonstrate reducing PM2.5 > 150 to PM2.5 < 50 using recommending PVC of approximately 20–74%. We achieved these results by periodically estimating and evaluating weather conditions in the autumn and winter as two critical seasons of the year.

中文翻译：

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一种智能算法，用于推荐减少 PM2.5 的植被覆盖百分比 (ARVC)

如今，不断增加的颗粒物 (PM) 仍然是对环境和人类健康的挑战，并增加了死亡统计数据。案例研究和实验观察表明，植被覆盖度和植物类型会影响 PM 和空气质量。PM2.5 的冷凝对空气质量恶化具有显着影响。增加植被覆盖率对减少 PM2.5 有显着影响。然而，植被覆盖率 (PVC) 需求百分比同样是一个阴影，需要仔细分析以推荐大都市不同部分的植被需求百分比和面积。在本文中，我们提出了一种四阶段智能算法，用于调查 PM2.5 和危急情况，以检测不健康的空气质量监测站 (AQMS)。我们的算法基于模糊和神经网络方法做出决定，并推荐植被的百分比密度和面积。我们对天气状况的分析是事件驱动的，将降雨视为一个事件，以检查降雨前后每个 AQMS 的情况。实验表明，使用推荐的 PVC 大约 20-74%，可将 PM2.5 > 150 降低到 PM2.5 < 50。我们通过定期估计和评估秋季和冬季作为一年中两个关键季节的天气条件来取得这些结果。50 使用大约 20–74% 的推荐 PVC。我们通过定期估计和评估秋季和冬季作为一年中两个关键季节的天气条件来取得这些结果。50 使用大约 20–74% 的推荐 PVC。我们通过定期估计和评估秋季和冬季作为一年中两个关键季节的天气条件来取得这些结果。