The development of low-cost sensors and novel calibration algorithms provides new hints to complement conventional ground-based observation sites to evaluate the spatial and temporal distribution of pollutants on hyperlocal scales (tens of meters). Here we use sensors deployed on a taxi fleet to explore the air quality in the road network of Nanjing over the course of a year (October 2019–September 2020). Based on GIS technology, we develop a grid analysis method to obtain 50 m resolution maps of major air pollutants (CO, NO 2 , and O 3 ). Through hotspot identification analysis, we find three main sources of air pollutants including traffic, industrial emissions, and cooking fumes. We find that CO and NO 2 concentrations show a pattern: highways > arterial roads > secondary roads > branch roads > residential streets, reflecting traffic volume. The O 3 concentrations in these five road types are in opposite order due to the titration effect of NO x . Combined the mobile measurements and the stationary station data, we diagnose that the contribution of traffic-related emissions to CO and NO 2 are 42.6 % and 26.3 %, respectively. Compared to the pre-COVID period, the concentrations of CO and NO 2 during the COVID-lockdown period decreased for 44.9 % and 47.1 %, respectively, and the contribution of traffic-related emissions to them both decreased by more than 50 %. With the end of the COVID-lockdown period, traffic emissions and air pollutant concentrations rebounded substantially, indicating that traffic emissions have a crucial impact on the variation of air pollutant levels in urban regions. This research demonstrates the sensing power of mobile monitoring for urban air pollution, which provides detailed information for source attribution, accurate traceability, and potential mitigation strategies at the urban micro-scale.

Urban air pollution has tremendous spatial variability at scales ranging from kilometers to meters due to unevenly distributed emission sources, complex flow patterns, and photochemical reactions. However, high-resolution air quality information is not available through traditional approaches such as ground-based measurements and regional air quality models (with typical resolution >  1 km). Here we develop a 10 m resolution air quality model for traffic-related CO pollution based on the Parallelized Large-Eddy Simulation Model (PALM). The model performance is evaluated with measurements obtained from sensors deployed on a taxi platform, which collects data with a comparable spatial resolution to our model. The very high resolution of the model reveals a detailed geographical dispersion pattern of air pollution in and out of the road network. The model results (0.92  ±  0.40 mg m −3 ) agree well with the measurements (0.90  ±  0.58 mg m −3 , n =114 502 ). The model has similar spatial patterns to those of the measurements, and the r 2 value of a linear regression between model and measurement data is 0.50  ±  0.07 during non-rush hours with middle and low wind speeds. A non-linear relationship is found between average modeled concentrations and wind speed with higher concentrations under calm wind speeds. The modeled concentrations are also 20 %–30 % higher in streets that align with the wind direction within ∼  20 ∘ . We find that streets with higher buildings downwind have lower modeled concentrations at the pedestrian level, and similar effects are found for the variability in building heights (including gaps between buildings). The modeled concentrations also decay fast in the first ∼  50 m from the nearest highway and arterial road but change slower further away. This study demonstrates the potential of large-eddy simulation in urban air quality modeling, which is a vigorous part of the smart city system and could inform urban planning and air quality management.

Recent studies have highlighted the importance of brown carbon (BrC) in various fields, particularly relating to climate change. The incomplete combustion of biomass in open and contained burning conditions is believed to be a significant contributor to primary BrC emissions. So far, few studies have reported the emission factors of BrC from biomass burning, and few studies have specifically addressed which form of light-absorbing carbon, such as black carbon (BC) or BrC, plays a leading role in the total solar light absorption by biomass burning. In this study, the optical integrating sphere (IS) approach was used, with carbon black and humic acid sodium salt as reference materials for BC and BrC, respectively, to distinguish BrC from BC on filter samples. A total of 11 widely used biomass types in China were burned in a typical stove to simulate the real household combustion process. (i) Large differences existed in the emission factors of BrC (EF BrC ) among the tested biomass fuels, with a geometric mean EF BrC of 0.71 g kg −1 (0.24–2.09). Both the plant type (herbaceous or ligneous) and burning style (raw or briquetted biomass) might influence the value of EF BrC . The observed reduction in the emissions of light-absorbing carbon (LAC) confirmed an additional benefit of biomass briquetting in climate change mitigation. (ii) The calculated annual BrC emissions from China's household biomass burning amounted to 712 Gg, higher than the contribution from China's household coal combustion (592 Gg). (iii) The average absorption Ångström exponent (AAE) was ( 2.46±0.53 ), much higher than that of coal-chunk combustion smoke ( AAE = 1.30 ± 0.32 ). (iv) For biomass smoke, the contribution of absorption by BrC to the total absorption by BC+BrC across the strongest solar spectral range of 350–850 nm ( F BrC ) was 50.8 %. This is nearly twice that for BrC in smoke from household coal combustion (26.5 %). (v) Based on this study, a novel algorithm was developed for estimating the F BrC for perhaps any combustion source ( F BrC = 0.5519 ln AAE + 0.0067 , R 2 =0.999 ); the F BrC value for all global biomass burning ( open+contained ) ( F BrC-entire ) was 64.5 % (58.5 %–69.9 %). This corroborates the dominant role of BrC in total biomass burning absorption. Therefore, the inclusion of BrC is not optional but indispensable when considering the climate energy budget, particularly for biomass burning emissions (contained and open).

Ovarian cancer is one of the most common malignancies often resulting in a poor prognosis. 5-methylcytosine (m5C) is a common epigenetic modification with roles in eukaryotes. However, the expression and function of m5C regulatory factors in ovarian cancer remained unclear. Two molecular subtypes with different prognostic and clinicopathological features were identified based on m5C regulatory factors. Meanwhile, functional annotation showed that in the two subtypes, 452 differentially expressed genes were significantly related to the malignant progression of ovarian cancer. Subsequently, four m5C genes were screened to construct a risk marker predictive of overall survival and indicative of clinicopathological features of ovarian cancer, also the robustness of the risk marker was verified in external dataset and internal validation set. multifactorial cox regression analysis and nomogram demonstrated that risk score was an independent prognostic factor for ovarian cancer prognosis. In conclusion, our results revealed that m5C-related genes play a critical role in tumor progression in ovarian cancer. Further detection of m5C methylation could provide a novel targeted therapy for treating ovarian cancer.

Recent studies have highlighted the importance of brown carbon (BrC) in various fields, particularly relating to climate change. The incomplete combustion of biomass in open and contained burning conditions is believed to be a significant contributor to primary BrC emissions. So far, few studies have reported the emission factors of BrC from biomass burning, and few studies have specifically addressed which form of light-absorbing carbon, such as black carbon (BC) or BrC, plays a leading role in the total solar light absorption by biomass burning. In this study, the optical integrating sphere (IS) approach was used, with carbon black and humic acid sodium salt as reference materials for BC and BrC, respectively, to distinguish BrC from BC on filter samples. A total of 11 widely used biomass types in China were burned in a typical stove to simulate the real household combustion process. (i) Large differences existed in the emission factors of BrC (EF BrC ) among the tested biomass fuels, with a geometric mean EF BrC of 0.71 g kg −1 (0.24–2.09). Both the plant type (herbaceous or ligneous) and burning style (raw or briquetted biomass) might influence the value of EF BrC . The observed reduction in the emissions of light-absorbing carbon (LAC) confirmed an additional benefit of biomass briquetting in climate change mitigation. (ii) The calculated annual BrC emissions from China's household biomass burning amounted to 712 Gg, higher than the contribution from China's household coal combustion (592 Gg). (iii) The average absorption Ångström exponent (AAE) was ( 2.46±0.53 ), much higher than that of coal-chunk combustion smoke ( AAE = 1.30 ± 0.32 ). (iv) For biomass smoke, the contribution of absorption by BrC to the total absorption by BC+BrC across the strongest solar spectral range of 350–850 nm ( F BrC ) was 50.8 %. This is nearly twice that for BrC in smoke from household coal combustion (26.5 %). (v) Based on this study, a novel algorithm was developed for estimating the F BrC for perhaps any combustion source ( F BrC = 0.5519 ln AAE + 0.0067 , R 2 =0.999 ); the F BrC value for all global biomass burning ( open+contained ) ( F BrC-entire ) was 64.5 % (58.5 %–69.9 %). This corroborates the dominant role of BrC in total biomass burning absorption. Therefore, the inclusion of BrC is not optional but indispensable when considering the climate energy budget, particularly for biomass burning emissions (contained and open).

Urban air pollution has tremendous spatial variability at scales ranging from kilometers to meters due to unevenly distributed emission sources, complex flow patterns, and photochemical reactions. However, high-resolution air quality information is not available through traditional approaches such as ground-based measurements and regional air quality models (with typical resolution >  1 km). Here we develop a 10 m resolution air quality model for traffic-related CO pollution based on the Parallelized Large-Eddy Simulation Model (PALM). The model performance is evaluated with measurements obtained from sensors deployed on a taxi platform, which collects data with a comparable spatial resolution to our model. The very high resolution of the model reveals a detailed geographical dispersion pattern of air pollution in and out of the road network. The model results (0.92  ±  0.40 mg m −3 ) agree well with the measurements (0.90  ±  0.58 mg m −3 , n =114 502 ). The model has similar spatial patterns to those of the measurements, and the r 2 value of a linear regression between model and measurement data is 0.50  ±  0.07 during non-rush hours with middle and low wind speeds. A non-linear relationship is found between average modeled concentrations and wind speed with higher concentrations under calm wind speeds. The modeled concentrations are also 20 %–30 % higher in streets that align with the wind direction within ∼  20 ∘ . We find that streets with higher buildings downwind have lower modeled concentrations at the pedestrian level, and similar effects are found for the variability in building heights (including gaps between buildings). The modeled concentrations also decay fast in the first ∼  50 m from the nearest highway and arterial road but change slower further away. This study demonstrates the potential of large-eddy simulation in urban air quality modeling, which is a vigorous part of the smart city system and could inform urban planning and air quality management.