Age Specific Exposure of Particulate Matters (PMs) Under Extreme Event in Three Different Cities of Western India

Abstract. The effect of climate change on population-weighted concentrations of particulate matter (PM) during extreme pollution events was studied using the Parallel Climate Model (PCM), the Weather Research and Forecasting (WRF) model and the UCD/CIT 3-D photochemical air quality model. A "business as usual" (B06.44) global emissions scenario was dynamically downscaled for the entire state of California between the years 2000–2006 and 2047–2053. Air quality simulations were carried out for 1008 days in each of the present-day and future climate conditions using year-2000 emissions. Population-weighted concentrations of PM0.1, PM2.5, and PM10 total mass, components species, and primary source contributions were calculated for California and three air basins: the Sacramento Valley air basin (SV), the San Joaquin Valley air basin (SJV) and the South Coast Air Basin (SoCAB). Results over annual-average periods were contrasted with extreme events. The current study found that the change in annual-average population-weighted PM2.5 mass concentrations due to climate change between 2000 vs. 2050 within any major sub-region in California was not statistically significant. However, climate change did alter the annual-average composition of the airborne particles in the SoCAB, with notable reductions of elemental carbon (EC; −3%) and organic carbon (OC; −3%) due to increased annual-average wind speeds that diluted primary concentrations from gasoline combustion (−3%) and food cooking (−4%). In contrast, climate change caused significant increases in population-weighted PM2.5 mass concentrations in central California during extreme events. The maximum 24-h average PM2.5 concentration experienced by an average person during a ten-yr period in the SJV increased by 21% due to enhanced production of secondary particulate matter (manifested as NH4NO3). In general, climate change caused increased stagnation during future extreme pollution events, leading to higher exposure to diesel engines particles (+32%) and wood combustion particles (+14%) when averaging across the population of the entire state. Enhanced stagnation also isolated populations from distant sources such as shipping (−61%) during extreme events. The combination of these factors altered the statewide population-averaged composition of particles during extreme events, with EC increasing by 23 %, nitrate increasing by 58%, and sulfate decreasing by 46%.

Climatic changes lead to many extreme weather events throughout the globe. These extreme weather events influence our behavior, exposing us to different environmental conditions, such as poor indoor quality. Poor indoor air quality (IAQ) poses a significant concern in the modern era, as people spend up to 90% of their time indoors. Ventilation influences key IAQ elements such as temperature, relative humidity, and particulate matter (PM). Children, considered a vulnerable group, spend approximately 30% of their time in educational settings, often housed in old structures with poorly maintained ventilation systems. Extreme weather events lead young students to stay indoors, usually behind closed doors and windows, which may lead to exposure to elevated levels of air pollutants. In our research, we aim to demonstrate how real-time monitoring of air pollutants and other environmental parameters under extreme weather is important for regulating the indoor environment. A study was conducted in a school building with limited ventilation located in an arid region near the Red Sea, which frequently suffers from high PM concentrations. In this study, we tracked the indoor environmental conditions and air quality during the entire month of May 2022, including an extreme outdoor weather event of sandstorms. During this month, we continuously monitored four classrooms in an elementary school built in 1967 in Eilat. Our findings indicate that PM2.5 was higher indoors (statistically significant) by more than 16% during the extreme event. Temperature was also elevated indoors (statistically significant) by more than 5%. The parameters’ deviation highlights the need for better indoor weather control and ventilation systems, as well as ongoing monitoring in schools to maintain healthy indoor air quality. This also warrants us as we are approaching an era of climatic instability, including higher occurrence of similar extreme events, which urge us to develop real-time responses in urban areas.

A significant concern for public health and visibility is airborne particulate matter, especially during extreme events. Of most relevance for health, air quality, and climate is the role of fine aerosol particles, specifically particulate matter with aerodynamic diameters less than or equal to 2.5 micrometers (PM2.5). The purpose of this study was to examine PM2.5 extreme events between 1989 and 2018 at Mesa Verde, Colorado using Interagency Monitoring of Protected Visual Environments (IMPROVE) monitoring data. Extreme events were identified as those with PM2.5 on a given day exceeding the 90th percentile value for that given month. We examine the weekly, monthly, and interannual trends in the number of extreme events at Mesa Verde, in addition to identifying the sources of the extreme events with the aid of the Navy Aerosol Analysis and Prediction (NAAPS) aerosol model. Four sources were used in the classification scheme: Asian dust, non-Asian dust, smoke, and “other”. Our results show that extreme PM2.5 events in the spring are driven mostly by the dust categories, whereas summertime events are influenced largely by smoke. The colder winter months have more influence from “other” sources that are thought to be largely anthropogenic in nature. No weekly cycle was observed for the number of events due to each source; however, interannual analysis shows that the relative amount of dust and smoke events compared to “other” events have increased in the last decade, especially smoke since 2008. The results of this work indicate that, to minimize and mitigate the effects of extreme PM2.5 events in the southwestern Colorado area, it is important to focus mainly on smoke and dust forecasting in the spring and summer months. Wintertime extreme events may be easier to regulate as they derive more from anthropogenic pollutants accumulating in shallow boundary layers in stagnant conditions.

Suspended particulate matter response to extreme forcings in the Bay of Seine

The Weather Research and Forecasting model with coupled Chemistry is used to study the impact of anthropogenic emission changes between 2005 and 2015 on historical extreme aerosol optical depth (AOD) events that occurred during 2003–2007 over the eastern USA. An ensemble of simulations is generated where individual and all combined emissions of SO2, NOx, and NH3 are perturbed relative to the 2005 levels for three subregions (Midwest, Northeast, and Southeast). These simulations are used to quantify fractional changes in the spatial and temporal characteristics of mean and peak AOD and near‐surface particulate matter (PM2.5), as well as changes in radiative forcing. Simulated AOD exhibits a spatially averaged decrease of 39%–63% during the six extreme events in response to the combined perturbed emissions. The impact on near‐surface PM2.5 concentrations is larger, with average decreases of ∼41%–69%. Peak AOD is reduced to below 1 in the perturbed simulations from initial values of 1.73–3.02 in the control runs driven by 2005 emissions. Radiative fluxes at the ground and top‐of‐the‐atmosphere exhibit considerably smaller and less consistent fractional changes across events, although changes in radiative fluxes during these extreme events are found to be larger than previously reported changes in seasonal mean values over the period 2005 to 2015.

Abstract. During winter 2013–2014 aerosol mass spectrometer (AMS) measurements were conducted for the first time with a novel PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 µm) lens in two major cities of China: Xi'an and Beijing. We denote the periods with visibility below 2 km as extreme haze and refer to the rest as reference periods. During the measurements in Xi'an an extreme haze covered the city for about a week and the total non-refractory (NR)-PM2.5 mass fraction reached peak concentrations of over 1000 µg m−3. During the measurements in Beijing two extreme haze events occurred, but the temporal extent and the total concentrations reached during these events were lower than in Xi'an. Average PM2.5 concentrations of 537 ± 146 and 243 ± 47 µg m−3 (including NR species and equivalent black carbon, eBC) were recorded during the extreme haze events in Xi'an and Beijing, respectively. During the reference periods the measured average concentrations were 140 ± 99 µg m−3 in Xi'an and 75 ± 61 µg m−3 in Beijing. The relative composition of the NR-PM2.5 evolved substantially during the extreme haze periods, with increased contributions of the inorganic components (mostly sulfate and nitrate). Our results suggest that the high relative humidity present during the extreme haze events had a strong effect on the increase of sulfate mass (via aqueous phase oxidation of sulfur dioxide). Another relevant characteristic of the extreme haze is the size of the measured particles. During the extreme haze events, the AMS showed much larger particles, with a volume weighted mode at about 800 to 1000 nm, in contrast to about 400 nm during reference periods. These large particle sizes made the use of the PM2.5 inlet crucial, especially during the severe haze events, where 39 ± 5 % of the mass would have been lost in the conventional PM1 (particulate matter with aerodynamic diameter ≤ 1 µm) inlet. A novel positive matrix factorization procedure was developed to apportion the sources of organic aerosols (OA) based on their mass spectra using the multilinear engine (ME-2) controlled via the source finder (SoFi). The procedure allows for an effective exploration of the solution space, a more objective selection of the best solution and an estimation of the rotational uncertainties. Our results clearly show an increase of the oxygenated organic aerosol (OOA) mass during extreme haze events. The contribution of OOA to the total OA increased from the reference to the extreme haze periods from 16.2 ± 1.1 to 31.3 ± 1.5 % in Xi'an and from 15.7 ± 0.7 to 25.0 ± 1.2 % in Beijing. By contrast, during the reference periods the total OA mass was dominated by domestic emissions of primary aerosols from biomass burning in Xi'an (42.2 ± 1.5 % of OA) and coal combustion in Beijing (55.2 ± 1.6 % of OA). These two sources are also mostly responsible for extremely high polycyclic aromatic hydrocarbon (PAH) concentrations measured with the AMS (campaign average of 2.1 ± 2.0 µg m−3 and frequent peak concentrations above 10 µg m−3). To the best of our knowledge, this is the first data set where the simultaneous extraction of these two primary sources could be achieved in China by conducting on-line AMS measurements at two areas with contrasted emission patterns.

Evaluating the impact of wildfire smoke on solar photovoltaic production

The role of long-term exposure to a comprehensive panel of particulate matter (PM) size fractions and extreme weather events in the development of incident arthritis remains underexplored. We aimed to evaluate their independent, combined, and interactive effects on arthritis risk in a Chinese middle-aged and older population. This prospective study utilized data from two nationally representative cohorts: The China Health and Retirement Longitudinal Study and the China Family Panel Studies. A total of 13,147 participants aged ≥ 45 years without baseline arthritis were included. Annual exposures to PM1, PM2.5, PM1-2.5, PM10, PM2.5-10, and extreme weather events (low/high-temperature days, extreme rainfall/drought days, and a composite Climate Physical Risk Index [CPRI]) were estimated using high-resolution datasets. Incident arthritis was identified via self-reported physician diagnosis. Time-updated Cox proportional hazards models were used to assess associations. Weighted quantile sum (WQS) regression evaluated the mixture effect, and additive interaction models quantified synergies between PM and extreme weather. Restricted cubic spline (RCS) and threshold effect analysis were utilized to evaluate potential nonlinear associations. Over a median follow-up, 1,200 incident arthritis cases occurred. Each 1µg/m³ increase in PM1, PM2.5, PM1-2.5, and PM10, was associated with hazard ratios (HRs) of 1.061 (95% CI: 1.030-1.093), 1.022 (95% CI: 1.011-1.033), 1.023 (95% CI: 1.009-1.037), and 1.004 (95% CI: 1.001-1.006), respectively. Additionally, each 1-day increase in extreme low-temperature day (LTD) was linked to HRs of 1.042 (95% CI: 1.019-1.065). The WQS mixture index yielded an HR of 1.719 (95% CI: 1.379-2.143), with PM2.5-10 contributing the largest weight (38.8%), followed by PM1-2.5 (34.7%) and LTD (25.4%). Dose-response curves revealed accelerating nonlinear relationships for PM1, PM2.5-10, LTD, extreme rainfall days (ERD), extreme drought days (EDD) and CPRI. In exploratory threshold analyses, above 40.862µg/m³ for PM₁, the HR per 1µg/m³ was 1.193 (95% CI: 1.131-1.259); for LTD above 17.624 days, the HR per additional day was 1.173 (95% CI: 1.147-1.199). For PM2.5₋₁₀, the threshold was 18.692µg/m³, with a strong association below (HR = 3.276, 95% CI: 2.338-4.590) and a marginal one above (HR = 1.005, 95% CI: 1.000-1.009). Significant additive interactions were observed between LTD and both PM10 (Synergy Index [S] = 2.129, 95% CI: 1.393-2.865) and PM2.5 (S = 1.867, 95% CI: 1.208-2.526). The association between PM and arthritis was modified by smoking and geographic region. Long-term exposure to ambient PM (across multiple size fractions) and extreme low-temperature weather are independent and synergistic risk factors for incident arthritis in middle-aged and older Chinese adults. Public health strategies targeting arthritis prevention should adopt an integrated approach addressing both air quality and climate resilience.

It has now been well established that the rise in global mercury has driven climate change phenomena that have led to extreme temperature events, sea level rise, change in the hydrological cycle, frequent droughts and floods, and cyclones and forest fires and caused a myriad of adverse impacts on vital worldwide sectors such as agriculture, water and health. The impact of climate change is anticipated to be more adverse for destitute and socioeconomically deprived populations from developing and underdeveloped nations owing to poor adaptive capacity and higher sensitivity. The present chapter focuses on the Indian context, where it presents shreds of evidence of the impact of climate change in the past, present and future such as extreme events like heat waves, diurnal temperature range, shrinking of Himalayan glaciers, shifting of rainfall patterns, increased susceptibility to floods and droughts, and its impact on some of the important sectors. The chapter shows clear evidence of a decline in crop production and productivity of some of the important crops such as wheat, rice, sugarcane, maize, potato, tomato, etc. The recent studies established an increase in morbidity and mortality associated with extreme temperature and poor air quality associated with increased particulate matter (PM), NOx, SOx, O3, black carbon and other ambient pollutants. In addition, important river basins of India, such as Gomti, Gandak, Vaigai, Mahi, Varuna and Ghaghra, have shown increased susceptibility to flooding and drought events that are more likely to be frequent and severe in the future under different climate change scenarios owing to changes in erratic rainfall patterns and increasing temperature. The chapter also discusses the potential adaptation and mitigation strategies that would help policymakers to combat climate change amid the rising susceptible population.

Modern sedimentation and geochemical imprints in sediments from the NW Madagascar margin

Abstract. Volatile organic compounds (VOCs) and particulate matter (PM) are major constituents of smog. Delhi experiences severe smog during the post-monsoon season, but a quantitative understanding of VOCs and PM sources is still lacking. Here, we conduct a source apportionment study for VOCs and PM using a recent (2022), high-quality dataset of 111 VOCs, PM2.5, and PM10 in a positive matrix factorization (PMF) model. Contrasts between clean monsoon air and polluted post-monsoon air, VOC source fingerprints, and molecular tracers enabled us to differentiate paddy residue burning from other biomass-burning sources, which had previously been impossible. Burning of fresh paddy residue, as well as residential heating and waste burning, contributed the most to observed PM10 levels (25 % and 23 %, respectively) and PM2.5 levels (23 % and 24 %, respectively), followed by heavy-duty vehicles fuelled by compressed natural gas (CNG), with a PM10 contribution of 15 % and a PM2.5 contribution of 11 %. For ambient VOCs, ozone formation potential, and secondary-organic-aerosol (SOA) formation potential, the top sources were petrol four-wheelers (20 %, 25 %, and 30 %, respectively), petrol two-wheelers (14 %, 12 %, and 20 %, respectively), industrial emissions (12 %, 14 %, and 15 %, respectively), solid-fuel-based cooking (10 %, 10 %, and 8 %, respectively), and road construction (8 %, 6 %, and 9 %, respectively). Emission inventories tended to overestimate residential biofuel emissions at least by a factor of 2 relative to the PMF output. The major source of PM pollution was regional biomass burning, while traffic and industries governed VOC emissions and secondary-pollutant formation. Our novel source apportionment method even quantitatively resolved similar biomass and fossil fuel sources, offering insights into both VOC and PM sources affecting extreme pollution events. This approach represents a notable advancement compared to current source apportionment approaches, and it could be of great relevance for future studies in other polluted cities and regions of the world with complex source mixtures.

Climate change is predicted to induce more extreme events such as storms, heat waves, drought and floods. Dust storms are frequently occurring in northern China. Those storms degrade air quality by decreasing visibility and inducing cardiovascular and respiratory diseases. To control dust storms, the Chinese government has launched a large-scale afforestation program by planting trees in arid areas, but the effectiveness of this program is still uncertain because the trajectories and altitudes of dust transport are poorly known. In particular, afforestation would be effective only if dust transport occurs at low altitudes. To test this hypothesis, we analyzed the extreme dust storm from May 2 to 7, 2017, which resulted in record-breaking dust loads over northern China. For that, we used dust RGB-composite data from the Himawari-8 satellite and the cloud-aerosol lidar, moderate-resolution imaging spectroradiometer data, and surface monitoring data. The source regions of the dust storms were identified using the hybrid single-particle Lagrangian integrated trajectory model and infrared pathfinder satellite observation. Contrary to our hypothesis, results show that dust is transported at high altitude of 1.0-6.5 km over long distances from northwestern China. This finding explains why the afforestation has not been effective to prevent this storm. Results also disclose the highest particulate matter (PM) concentrations of 447.3 μg/m 3 for PM 2.5 and 1842.0 μg/m 3 for PM 10 during the dust storm. Those levels highly exceed Chinese ambient air quality standards of 75 μg/m 3 for PM 2.5 and 150 μg/m 3 for PM 10 .

The influence of extreme dust events in the current and future 100% renewable power scenarios in Tenerife

In the context of global warming and rapid urbanization, the frequency of simultaneous occurrence of extreme high temperature, ozone pollution, and particulate matter pollution has increased. However, independent and composite characterization of PM2.5, ozone, and extreme heat pollution events has not been systematically analyzed so far. This study combines meteorological and pollutant data with the GTWR model in an attempt to reveal the patterns of independent heat days (IHD), compound PM2.5-ozone pollution (CPOP), and composite heat-PM2.5-ozone pollution (CHPOP). In this study, we found that in July and August in South Korea, the frequency of CPOP events, the frequency of CHPOP events, and the composite proportion of CHPOP events all show an overall pattern of east-high and west-low; the atmospheric circulation patterns of the three extreme events have brought about more stagnation conditions, which may be related to cyclone activity; the occurrence of CPOP events is mainly accompanied by a continuous decrease in relative humidity and cloud cover, both IHD and CHPOP events occur with increasing temperatures, decreasing cloudiness, and anomalously high pressures; under the same events, excluding relative humidity, PM2.5 and ozone showed similar conditions with respect to the dependence on temperature, wind speed, barometric pressure, cloudiness, and nitrogen dioxide. This study identified the independent and composite characteristics of PM2.5, ozone, and extreme heat pollution events, which can enhance early prediction and pollution prevention of these extreme events.

<p>Black carbon (BC) aerosol emission is an important contributor to particulate matter (PM) pollution in China, leading to adverse health effects and premature deaths. BC aerosols can also affect boundary layer meteorology by heating the atmosphere, due to the unique property of BC to absorb solar radiation. In contrast, sulphate aerosols reflect solar radiation and thus cool the surface. How individual aerosol pollutants influence boundary layer meteorology on a multi-year timescale is not well known. A particularly important aspect of this influence is a potential feedback process, where changed boundary layer conditions may influence present aerosol concentrations, potentially exacerbating near-surface pollution levels. In this work, we use the Weather Research and Forecasting model with Chemistry (WRF-Chem) at 45 km horizontal resolution covering East and South Asia, and at 15 km resolution covering East China. Simulations are driven by the ECMWF Reanalysis v5 (ERA5), and anthropogenic emissions are from the latest version of the Community Emissions Data System (CEDS). Multi-year simulations are evaluated against observations of meteorological parameters and air quality data for China. Preliminary results show that aerosol-radiation interactions due to BC lead to higher annual near-surface PM concentrations, underscoring the importance of mitigating black carbon aerosol emissions. The elevated PM concentrations can be explained by a shallower boundary layer and reduced turbulent mixing near the surface associated with BC. Possible effects of aerosol-radiation interactions on extreme pollution events, including not only extreme PM events but also extreme ozone (O3) events, will be examined.</p>