Insights into the source contributions to the elevated fine particulate matter in Nigeria using a source-oriented chemical transport model.

In 2021, Nigeria was ranked by the World Health Organization (WHO) as one of the top countries with highly deteriorating air quality in the world. To date, no study has elucidated the sources of elevated fine particulate matter (PM2.5) concentrations over the entire Nigeria. In this study, the Community Multiscale Air Quality (CMAQ) model was applied to quantify the contributions of seven emissions sectors to PM2.5 and its components in Nigeria in 2021. Residential, industry, and agriculture were the major sources of primary PM (PPM) during the four seasons, elemental carbon (EC) and primary organic carbon (POC) were dominated by residential and industry, while residential, industry, transportation, and agriculture were the important sources of secondary inorganic aerosols (SIA) and its components in most regions. PM2.5 was up to 150 µg/m3 in the north in all the seasons, while it reached ∼80 µg/m3 in the south in January. Residential contributed most to PM2.5 (∼80 µg/m3), followed by industry (∼40 µg/m3), transportation (∼20 µg/m3), and agriculture (∼15 µg/m3). The large variation in the sources of PM2.5 and its components across Nigeria suggests that emissions control strategies should be separately designed for different regions. The results imply that urgent control of PM2.5 pollution in Nigeria is highly necessitated.

Multi-site, multi-pollutant atmospheric data analysis using Riemannian geometry.

We demonstrate the benefits of using Riemannian geometry in the analysis of multi-site, multi-pollutant atmospheric monitoring data. Our approach uses covariance matrices to encode spatio-temporal variability and correlations of multiple pollutants at different sites and times. A key property of covariance matrices is that they lie on a Riemannian manifold and one can exploit this property to facilitate dimensionality reduction, outlier detection, and spatial interpolation. Specifically, the transformation of data using Reimannian geometry provides a better data surface for interpolation and assessment of outliers compared to traditional data analysis tools that assume Euclidean geometry. We demonstrate the utility of using Riemannian geometry by analyzing a full year of atmospheric monitoring data collected from 34 monitoring stations in Beijing, China.

Quantitative estimation of meteorological impacts and the COVID-19 lockdown reductions on NO2 and PM2.5 over the Beijing area using Generalized Additive Models (GAM).

Unprecedented travel restrictions due to the COVID-19 pandemic caused remarkable reductions in anthropogenic emissions, however, the Beijing area still experienced extreme haze pollution even under the strict COVID-19 controls. Generalized Additive Models (GAM) were developed with respect to inter-annual variations, seasonal cycles, holiday effects, diurnal profile, and the non-linear influences of meteorological factors to quantitatively differentiate the lockdown effects and meteorology impacts on concentrations of nitrogen dioxide (NO2) and fine particulate matters (PM2.5) at 34 sites in the Beijing area. The results revealed that lockdown measures caused large reductions while meteorology offset a large fraction of the decrease in surface concentrations. GAM estimates showed that in February, the control measures led to average NO2 reductions of 19 µg/m3 and average PM2.5 reductions of 12 µg/m3. At the same time, meteorology was estimated to contribute about 12 µg/m3 increase in NO2, thereby offsetting most of the reductions as well as an increase of 30 µg/m3 in PM2.5, thereby resulting in concentrations higher than the average PM2.5 concentrations during the lockdown. At the beginning of the lockdown period, the boundary layer height was the dominant factor contributing to a 17% increase in NO2 while humid condition was the dominant factor for PM2.5 concentrations leading to an increase of 65% relative to the baseline level. Estimated NO2 emissions declined by 42% at the start of the lockdown, after which the emissions gradually increased with the increase of traffic volumes. The diurnal patterns from the models showed that the peak of vehicular traffic occurred from about 12pm to 5pm daily during the strictest control periods. This study provides insights for quantifying the changes in air quality due to the lockdowns by accounting for meteorological variability and providing a reference in evaluating the effectiveness of control measures, thereby contributing to air quality mitigation policies.

Competing PM2.5 and NO2 holiday effects in the Beijing area vary locally due to differences in residential coal burning and traffic patterns.

The holiday effect is a useful tool to estimate the impact on air pollution due to changes in human activities. In this study, we assessed the variations in concentrations of fine particulate matter (PM2.5) and nitrogen dioxide (NO2) during the holidays in the heating season from 2014 to 2018 based on daily surface air quality monitoring measurements in Beijing. A Generalized Additive Model (GAM) is used to analyze pollutant concentrations for 34 sites by comprehensively accounting for annual, monthly, and weekly cycles as well as the nonlinear impacts of meteorological factors. A Saturday effect was found in the downtown area, with about 4% decrease in PM2.5 and 3% decrease in NO2 relative to weekdays. On Sundays, the PM2.5 concentrations increased by about 5% whereas there were no clear changes for NO2. In contrast to the small effect of the weekend, there was a strong holiday effect throughout the region with average increases of about 22% in PM2.5 and average reductions of about 11% in NO2 concentrations. There was a clear geographical pattern in the strength of the holiday effect. In rural areas the increase in PM2.5 is related to the proportion of coal and biomass consumption for household heating. In the suburban areas between the Fifth Ring Road and Sixth Ring Road there were larger reductions in NO2 than downtown which might be due to decreased traffic as many people return to their hometown for the holidays. This study provides insights into the pattern of changes in air pollution due to human activities. By quantifying the changes, it also provides insights for improvements in air quality due to control policies implemented in Beijing during the heating season.

COVID-19 pandemic in Wuhan: Ambient air quality and the relationships between criteria air pollutants and meteorological variables before, during, and after lockdown.

As a result of the lockdown (LD) control measures enacted to curtail the COVID-19 pandemic in Wuhan, almost all non-essential human activities were halted beginning on January 23, 2020 when the total lockdown was implemented. In this study, changes in the concentrations of the six criteria air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3) in Wuhan were investigated before (January 1 to 23, 2020), during (January 24 to April 5, 2020), and after the COVID-19 lockdown (April 6 to June 20, 2020) periods. Also, the relationships between the air pollutants and meteorological variables during the three periods were investigated. The results showed that there was significant improvement in air quality during the lockdown. Compared to the pre-lockdown period, the concentrations of NO2, PM2.5, PM10, and CO decreased by 50.6, 41.2, 33.1, and 16.6%, respectively, while O3 increased by 149% during the lockdown. After the lockdown, the concentrations of PM2.5, CO and SO2 declined by an additional 19.6, 15.6, and 2.1%, respectively. However, NO2, O3, and PM10 increased by 55.5, 25.3, and 5.9%, respectively, compared to the lockdown period. Except for CO and SO2, WS had negative correlations with the other pollutants during the three periods. RH was inversely related with all pollutants. Positive correlations were observed between temperature and the pollutants during the lockdown. Easterly winds were associated with peak PM2.5 concentrations prior to the lockdown. The highest PM2.5 concentrations were associated with southwesterly wind during the lockdown, and northwesterly winds coincided with the peak PM2.5 concentrations after the lockdown. Although, COVID-19 pandemic had numerous negative effects on human health and the global economy, the reductions in air pollution and significant improvement in ambient air quality likely had substantial short-term health benefits. This study improves the understanding of the mechanisms that lead to air pollution under diverse meteorological conditions and suggest effective ways of reducing air pollution in Wuhan.

Associations between source-resolved PM2.5 and airway inflammation at urban and rural locations in Beijing.

BACKGROUND: A large number of research studies have explored the health effects of exposure to atmospheric particulate matter. However, limited quantitative evidence has linked specific sources of personal PM2.5 directly to adverse health effects. This study was conducted in order to examine the association between airway inflammation and personal exposure to PM2.5 mass, components, and sources among two healthy cohorts living in both urban and rural areas of Beijing, China. METHODS: We conducted a follow-up study during the summer of 2016 and the winter of 2016/2017 among 92 students and 43 guards. 24-h personal and ambient exposure to PM2.5 and fractional exhaled nitric oxide (FeNO) were measured at least twice for each participant. Chemical components of 385 personal PM2.5 exposure samples were analyzed, and pollution sources were resolved by a positive matrix factorization (PMF) receptor model. We have constructed linear mixed effect models to evaluate the association between ambient/personal PM2.5 mass, chemical constituents, and source specific PM2.5 with FeNO after controlling for temperature, relative humidity, sites, season, and potential individual confounders. RESULTS: Interquartile range (IQR) increase in household heating sources was associated with increased FeNO (2.72%; 95% CI = 1.26-4.17%) across two sites. IQR increase in roadway transport was associated with increased FeNO (9.84%; 95% CI = 2.69-17%) in urban areas; IQR increase in Secondary inorganic sources and Industrial/Combustion sources were associated with increased FeNO (7.96%; 95% CI = 1.47-14.4%% and 7.85%; 95% CI = 0.0676-15.6%, respectively) in rural areas. Personal exposure to EC, OC, and some trace elements (Se, Pb, Bi, Cs) were also estimated to be significantly associated with the increase of FeNO. In addition, there was no significant difference (P > 0.05) between the effects of ambient and personal PM2.5 mass. CONCLUSIONS: Although personal PM2.5 mass was not significantly associated with the health effects, airway inflammation can be linked to source-resolved exposures.

Chemical characterization and source apportionment of PM2.5 personal exposure of two cohorts living in urban and suburban Beijing.

In the study, personal PM2.5 exposures and their source contributions were characterized for 159 subjects living in the Beijing Metropolitan area. The exposures and sources were examined as functions of residential location, season, vocation, cigarette smoking, and time spent outdoors. Sampling was performed for two categories of volunteers, guards and students, that lived in urban and suburban areas of Beijing. Samples were collected using portable PM2.5 monitors during summer and winter. Exposure measurements were supplemented with a questionnaire that tracked personal activity and time spent in microenvironments that may have impacted exposures. Simultaneously, ambient PM2.5 data were obtained from national network stations located at the Gucheng and Huairouzhen sites. These data were used as a comparison against the personal PM2.5 exposures and produced poor correlations between personal and ambient PM2.5. These results demonstrate that individual behavior strongly affects personal PM2.5 exposure. Six primary sources of personal PM2.5 exposure were determined using a positive matrix factorization (PMF) source apportionment model. These sources included Roadway Transport Source, Soil/Dust Source, Industrial/Combustion Source, Secondary Inorganic Source, Cd Source, and Household Heating Source. Averaged across all subjects and seasons, the highest source contribution was Secondary Inorganic Source (24.8% ±â€¯32.6%, AVG ±â€¯STD), whereas the largest primary ambient source was determined to be Roadway Transport (20.9% ±â€¯13.6%). Subjects were classified according to the questionnaire and were used to help understand the relationship between personal activity and source contribution to PM2.5 exposure. In general, primary ambient sources showed only significant spatial and seasonal differences, while secondary sources differed significantly between populations with different personal behavior. In particular, Cd source was found to be related to smoking exposure and was the most unpredictable source, with significant differences between populations of different sites, vocations, smoking exposures, and outdoor time.