A new methodology to assess the performance and uncertainty of source apportionment models II: The results of two European intercomparison exercises

The performance and the uncertainty of receptor models (RMs) were assessed in intercomparison exercises employing real-world and synthetic input datasets. To that end, the results obtained by different practitioners using ten different RMs were compared with a reference. In order to explain the differences in the performances and uncertainties of the different approaches, the apportioned mass, the number of sources, the chemical profiles, the contribution-to-species and the time trends of the sources were all evaluated using the methodology described in Belis et al. (2015). In this study, 87% of the 344 source contribution estimates (SCEs) reported by participants in 47 different source apportionment model results met the 50% standard uncertainty quality objective established for the performance test. In addition, 68% of the SCE uncertainties reported in the results were coherent with the analytical uncertainties in the input data. The most used models, EPA-PMF v.3, PMF2 and EPA-CMB 8.2, presented quite satisfactory performances in the estimation of SCEs while unconstrained models, that do not account for the uncertainty in the input data (e.g. APCS and FA-MLRA), showed below average performance. Sources with well-defined chemical profiles and seasonal time trends, that make appreciable contributions (>10%), were those better quantified by the models while those with contributions to the PM mass close to 1% represented a challenge. The results of the assessment indicate that RMs are capable of estimating the contribution of the major pollution source categories over a given time window with a level of accuracy that is in line with the needs of air quality management

Statistical Emulation of Winter Ambient Fine Particulate Matter Concentrations From Emission Changes in China

Air pollution exposure remains a leading public health problem in China. The use of chemical transport models to quantify the impacts of various emission changes on air quality is limited by their large computational demands. Machine learning models can emulate chemical transport models to provide computationally efficient predictions of outputs based on statistical associations with inputs. We developed novel emulators relating emission changes in five key anthropogenic sectors (residential, industry, land transport, agriculture, and power generation) to winter ambient fine particulate matter (PM2.5) concentrations across China. The emulators were optimized based on Gaussian process regressors with Matern kernels. The emulators predicted 99.9% of the variance in PM2.5 concentrations for a given input configuration of emission changes. PM2.5 concentrations are primarily sensitive to residential (51%–94% of first‐order sensitivity index), industrial (7%–31%), and agricultural emissions (0%–24%). Sensitivities of PM2.5 concentrations to land transport and power generation emissions are all under 5%, except in South West China where land transport emissions contributed 13%. The largest reduction in winter PM2.5 exposure for changes in the five emission sectors is by 68%–81%, down to 15.3–25.9 μg m−3, remaining above the World Health Organization annual guideline of 10 μg m−3. The greatest reductions in PM2.5 exposure are driven by reducing residential and industrial emissions, emphasizing the importance of emission reductions in these key sectors. We show that the annual National Air Quality Target of 35 μg m−3 is unlikely to be achieved during winter without strong emission reductions from the residential and industrial sectors

Environmental pollution policy of small businesses in Nigeria and Ghana: extent and impact

This study provides a comprehensive assessment of firms’ operation and environmental protection polices in Nigeria and Ghana, where there has been a rising industrial growth amidst low regulatory and institutional frameworks. We analyze the extents to which firms’ adoption of environmental protection policies affect their performances. We use firm-level data of 842 firms (447 for Nigeria and 395 for Ghana) distributed across different regions of both countries for our descriptive and econometric estimations. We find, among other things, that firms’ adoption of internal policies on environmental protection is dismally low in both Nigeria (32%) and Ghana (17%), with policies focused on reducing solid (38%, Nigeria; and 35%, Ghana), gaseous (22%, Nigeria; and 44%, Ghana), and liquid (24%, Nigeria; and 14%, Ghana) pollution. Training appears to be an important intervention that can help improve firms’ adoption of such policies. We also found that firms’ adoption and implementation of environmental protection policies significantly improve their performance

The heterogeneous chemical kinetics of N₂O₅ on CaCO₃ and other atmospheric mineral dust surrogates

International audienceUptake experiments of N2O5 on several mineral dust powder samples were carried out under continuous molecular flow conditions at 298±2 K. At [N2O5]0=(4.0±1.0)×1011 cm?3 we have found ?ss values ranging from (3.5±1.1)×10?2 for CaCO3 to (0.20±0.05) for Saharan Dust with ?ss decreasing as [N2O5]0 increased. The uptake coefficients reported in this work are to be regarded as upper limiting values owing to the fact that they are based on the geometric (projected) surface area of the mineral dust sample. We have observed delayed production of HNO3 upon uptake of N2O5 for every investigated sample owing to hydrolysis of N2O5 with surface-adsorbed H2O. Arizona Test Dust and Kaolinite turned out to be the samples that generated the largest amount of gas phase HNO3 with respect to N2O5 taken up. In contrast, the yield of HNO3 for Saharan Dust and CaCO3 is lower. On CaCO3 the disappearance of N2O5 was also accompanied by the formation of CO2. For CaCO3 sample masses ranging from 0.33 to 2.0 g, the yield of CO2 was approximately 42?50% with respect to the total number of N2O5 molecules taken up. The reaction of N2O5 with mineral dust and the subsequent production of gas phase HNO3 lead to a decrease in [NOx] which may have a significant effect on global ozone

Pedestrian analysis for crowd monitoring: the Milan case study (Italy)

Advanced technologies for monitoring and predicting pedestrian behaviors in public areas have gradually spread to ensure safe and efficient mobility in modern societies. Recently, the Covid-19 pandemic has further highlighted the potential of crowd analysis and monitoring tools, especially in gathering places where a minimum spacing has to be guaranteed, such as in transportation terminals. In this paper, starting from data acquired by a video recording system located outside the Milano Centrale railway station (Italy), several analyses have been conducted in order to investigate macroscopic parameters of pedestrian dynamics (i.e. densities, speeds and main walking directions), as well as testing strengths and weaknesses of Computer Vision algorithms for pedestrian detection. Results demonstrated that the precision of the system is reliable, as most of the people were "partially"tracked and the analysis of the macroscopic parameters are useful to identify effective intervention strategies in emergency and crowded situations. The analyses also deliver first insights for the development of a decision support system aimed at managing and supervising pedestrian dynamics