Retrieval of black carbon aerosol surface concentration using integrated MODIS and AERONET data

<p>Black Carbon (BC) is a carbonaceous aerosol that strongly absorbs solar radiation. The high emissions of these highly absorbent particles exacerbate regional air quality and pose significant threats to global climate, both in the short and long term. Therefore, accurately quantifying the spatial distribution of BC is crucial for improving regional air quality and mitigating the climate change impacts driven by human activities. In this study, we developed a novel algorithm for retrieving BC surface concentration jointly using MODIS and AERONET data. Firstly, the algorithm employed the <span class="inline-formula"><i>K</i></span>-means clustering method to determine seasonal background aerosols model based on AERONET V3 daily products. Then, the Maxwell–Garnett effective medium approximation model was utilized to calculate the complex refractive index of the internally mixed aerosols. Subsequently, the lookup tables were established using the 6SV2.1 radiative transfer code to estimate optimal BC fraction and column concentration. Next, the column concentration data were converted to surface concentration using a conversion coefficient derived from MERRA-2. Finally, the retrieved MODIS BC surface concentration was validated with in-situ Aethalometer measurements. The validation showed a correlation coefficient (<span class="inline-formula"><i>R</i></span>) of 0.727, a root mean square error (RMSE) of 0.353, a mean absolute error (MAE) of 0.211, and a linear fit function of <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M3" display="inline" overflow="scroll" dspmath="mathml"><mrow><mi>y</mi><mo>=</mo><mn mathvariant="normal">0.718</mn><mi>x</mi><mo>+</mo><mn mathvariant="normal">0.015</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="94pt" height="12pt" class="svg-formula" dspmath="mathimg" md5hash="f759658ddf68323bf08da6f7eff26227"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="amt-18-4559-2025-ie00001.svg" width="94pt" height="12pt" src="amt-18-4559-2025-ie00001.png"/></svg:svg></span></span>. These statistical parameters outperform those obtained from MERRA-2 BC data (<span class="inline-formula"><i>R</i></span> <span class="inline-formula">=</span> 0.655, RMSE <span class="inline-formula">=</span> 0.487, MAE <span class="inline-formula">=</span> 0.381, and <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M8" display="inline" overflow="scroll" dspmath="mathml"><mrow><mi>y</mi><mo>=</mo><mn mathvariant="normal">0.686</mn><mi>x</mi><mo>+</mo><mn mathvariant="normal">0.400</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="94pt" height="12pt" class="svg-formula" dspmath="mathimg" md5hash="d6c1f20f4d6fa1eee32c84e3680a591d"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="amt-18-4559-2025-ie00002.svg" width="94pt" height="12pt" src="amt-18-4559-2025-ie00002.png"/></svg:svg></span></span>), demonstrating the superior performance of the proposed algorithm in this study area.</p>