High resolution mapping of PM2.5 concentration over Tehran city is
challenging because of the complicated behavior of numerous sources of
pollution and the insufficient number of ground air quality monitoring
stations. Alternatively, high resolution satellite Aerosol Optical Depth (AOD)
data can be employed for high resolution mapping of PM2.5. For this purpose,
different data-driven methods have been used in the literature. Recently, deep
learning methods have demonstrated their ability to estimate PM2.5 from AOD
data. However, these methods have several weaknesses in solving the problem of
estimating PM2.5 from satellite AOD data. In this paper, the potential of the
deep ensemble forest method for estimating the PM2.5 concentration from AOD
data was evaluated. The results showed that the deep ensemble forest method
with R2 = 0.74 gives a higher accuracy of PM2.5 estimation than deep learning
methods (R2 = 0.67) as well as classic data-driven methods such as random
forest (R2 = 0.68). Additionally, the estimated values of PM2.5 using the deep
ensemble forest algorithm were used along with ground data to generate a high
resolution map of PM2.5. Evaluation of the produced PM2.5 map revealed the good
performance of the deep ensemble forest for modeling the variation of PM2.5 in
the city of Tehran.

用于估计 PM2.5 浓度的深度集成森林方法在提供高分辨率 PM2.5 地图的建模方面表现出较好的性能，相较于深度学习方法和经典的数据驱动方法，其能够给出更准确的 PM2.5 估计值。

利用深度集合森林技术，对伊朗德黑兰的 MODIS MAIAC AOD 数据进行 PM2.5 高分辨率制图

Using Deep Ensemble Forest for High Resolution Mapping of PM2.5 from  MODIS MAIAC AOD in Tehran, Iran

The prevalence and mobility of smartphones make these a widely used tool for
environmental health research. However, their potential for determining
aggregated air quality index (AQI) based on PM2.5 concentration in specific
locations remains largely unexplored in the existing literature. In this paper,
we thoroughly examine the challenges associated with predicting
location-specific PM2.5 concentration using images taken with smartphone
cameras. The focus of our study is on Dhaka, the capital of Bangladesh, due to
its significant air pollution levels and the large population exposed to it.
Our research involves the development of a Deep Convolutional Neural Network
(DCNN), which we train using over a thousand outdoor images taken and
annotated. These photos are captured at various locations in Dhaka, and their
labels are based on PM2.5 concentration data obtained from the local US
consulate, calculated using the NowCast algorithm. Through supervised learning,
our model establishes a correlation index during training, enhancing its
ability to function as a Picture-based Predictor of PM2.5 Concentration (PPPC).
This enables the algorithm to calculate an equivalent daily averaged AQI index
from a smartphone image. Unlike, popular overly parameterized models, our model
shows resource efficiency since it uses fewer parameters. Furthermore, test
results indicate that our model outperforms popular models like ViT and INN, as
well as popular CNN-based models such as VGG19, ResNet50, and MobileNetV2, in
predicting location-specific PM2.5 concentration. Our dataset is the first
publicly available collection that includes atmospheric images and
corresponding PM2.5 measurements from Dhaka. Our code and dataset will be made
public when publishing the paper.

本研究通过使用智能手机相机拍摄的图像，使用深度卷积神经网络（DCNN）开发了一个基于图片的 PM2.5 浓度预测模型，该模型在预测特定位置的 PM2.5 浓度方面优于其他流行的模型，并提供了来自达卡的大气图像和相应 PM2.5 测量结果的公开数据集。