Particulate matter air pollution may offset ozone damage to global crop production

Abstract. Ensuring global food security requires a comprehensive understanding of environmental pressures on food production, including the impacts of air quality. Surface ozone damages plants and decreases crop production; this effect has been extensively studied. In contrast, the presence of particulate matter (PM) in the atmosphere can be beneficial to crops given that enhanced light scattering leads to a more even and efficient distribution of photons which can outweigh total incoming radiation loss. This study quantifies the impacts of ozone and PM on the global production of maize, rice, and wheat in 2010 and 2050. We show that accounting for the growing season of these crops is an important factor in determining their air pollution exposure. We find that the effect of PM can offset much, if not all, of the reduction in yield associated with ozone damage. Assuming maximum sensitivity to PM, the current (2010) global net impact of air quality on crop production varies by crop (+5.6, −3.7, and +4.5 % for maize, wheat, and rice, respectively). Future emissions scenarios indicate that attempts to improve air quality can result in a net negative effect on crop production in areas dominated by the PM effect. However, we caution that the uncertainty in this assessment is large, due to the uncertainty associated with crop response to changes in diffuse radiation; this highlights that a more detailed physiological study of this response for common cultivars is crucial.

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Particulate matter air pollution offsets ozone damage to global crop production

10.5194/acp-2017-1073 ◽

2017 ◽

Author(s):

Luke D. Schiferl ◽

Colette L. Heald

Keyword(s):

Air Pollution ◽

Particulate Matter ◽

Air Quality ◽

Crop Production ◽

Surface Ozone ◽

Diffuse Radiation ◽

Environmental Pressures ◽

Global Food Security ◽

Negative Effect ◽

Pollution Exposure

Abstract. Ensuring global food security requires a comprehensive understanding of environmental pressures on food production, including the impacts of air quality. Surface ozone damages plants and decreases crop production; this effect has been extensively studied. In contrast, the presence of particulate matter (PM) in the atmosphere can be beneficial to crops given that enhanced light scattering leads to a more even and efficient distribution of photons which can outweigh total incoming radiation loss. This study quantifies the impacts of ozone and PM on the global production of maize, rice, and wheat in 2010 and 2050. We show that accounting for the growing season of these crops is an important factor in determining their air pollution exposure. We find that the effect of PM can offset much, if not all, of the reduction in yield associated with ozone damage. Assuming maximum sensitivity to PM, the current (2010) global net impact of air quality on crop production is positive (+6.0 %, +0.5 %, and +4.9 % for maize, wheat, and rice, respectively). Future emissions scenarios indicate that attempts to improve air quality can result in a net negative effect on crop production in areas dominated by the PM effect. However, we caution that the uncertainty in this assessment is large due to the uncertainty associated with crop response to changes in diffuse radiation; this highlights that more detailed physiological study of this response for common cultivars is crucial.

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Long‐Term Exposure to Particulate Air Pollution Is Associated With 30‐Day Readmissions and Hospital Visits Among Patients With Heart Failure

Journal of the American Heart Association ◽

10.1161/jaha.120.019430 ◽

2021 ◽

Author(s):

Cavin K. Ward‐Caviness, ◽

Mahdieh Danesh Yazdi, ◽

Joshua Moyer, ◽

Anne M. Weaver, ◽

Wayne E. Cascio, ◽

...

Keyword(s):

Heart Failure ◽

Air Pollution ◽

Particulate Matter ◽

Fine Particulate Matter ◽

Total Hospital ◽

Fine Particulate ◽

Patients With Heart Failure ◽

Hospital Visits ◽

Pollution Exposure

Background Long‐term air pollution exposure is a significant risk factor for inpatient hospital admissions in the general population. However, we lack information on whether long‐term air pollution exposure is a risk factor for hospital readmissions, particularly in individuals with elevated readmission rates. Methods and Results We determined the number of readmissions and total hospital visits (outpatient visits+emergency room visits+inpatient admissions) for 20 920 individuals with heart failure. We used quasi‐Poisson regression models to associate annual average fine particulate matter at the date of heart failure diagnosis with the number of hospital visits and 30‐day readmissions. We used inverse probability weights to balance the distribution of confounders and adjust for the competing risk of death. Models were adjusted for age, race, sex, smoking status, urbanicity, year of diagnosis, short‐term fine particulate matter exposure, comorbid disease, and socioeconomic status. A 1‐µg/m 3 increase in fine particulate matter was associated with a 9.31% increase (95% CI, 7.85%–10.8%) in total hospital visits, a 4.35% increase (95% CI, 1.12%–7.68%) in inpatient admissions, and a 14.2% increase (95% CI, 8.41%–20.2%) in 30‐day readmissions. Associations were robust to different modeling approaches. Conclusions These results highlight the potential for air pollution to play a role in hospital use, particularly hospital visits and readmissions. Given the elevated frequency of hospitalizations and readmissions among patients with heart failure, these results also represent an important insight into modifiable environmental risk factors that may improve outcomes and reduce hospital use among patients with heart failure.

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Impact of Diwali firework emissions on air quality of New Delhi, India during 2013-2015

MAUSAM ◽

10.54302/mausam.v68i1.438 ◽

2021 ◽

Vol 68 (1) ◽

pp. 111-118

Author(s):

SUNIL KUMAR PESHIN ◽

PRIYANKA SINHA ◽

AMIT BISHT

Keyword(s):

Particulate Matter ◽

Air Quality ◽

Surface Ozone ◽

Average Concentration ◽

New Delhi ◽

Oxides Of Nitrogen ◽

Huge Amount ◽

Awareness Campaigns ◽

Pm10 And Pm2.5

Diwali is one of the major and most important festivals celebrated all over India which falls in the period late October to early November every year. It is associated with burning of firecrackers especially during the night of Diwali day that leads to degradation of air quality that lasts for a longer duration of time. Firecrackers on burning releases huge amount of trace gases such as NOx, CO, SO2 and O3 and huge amount of aerosols and particulate matter. The present study focuses on the influence of firecrackers emissions on surface ozone(O3) ,oxides of nitrogen (NOx) and particulate matter (PM10 and PM2.5)concentration over the capital urban metropolis of India, New Delhi during Diwali festivity period from 2013-2015. A sharp increase is observed in surface ozone, NOx and particulate matter concentration during the Diwali day as compared to control day for 2013 to 2015 which is mainly attributed to burning of firecrackers. However the average concentration levels of the gaseous pollutants and particulate matter (PM10 and PM2.5) on Diwali day exhibited a decline in 2015 and 2014 as compared to 2013 due to increase in awareness campaigns among public and increased cost of firecrackers.

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Impacts of emission changes in China from 2010 to 2017 on domestic and intercontinental air quality and health effect

10.5194/acp-2021-385 ◽

2021 ◽

Author(s):

Yuqiang Zhang ◽

Drew Shindell ◽

Karl Seltzer ◽

Lu Shen ◽

Jean-Francois Lamarque ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Air Pollutants ◽

Transport Model ◽

Surface Ozone ◽

Health Effect ◽

Premature Deaths ◽

Concentration Changes ◽

Emission Changes ◽

Related Mortality

Abstract. China has seen dramatic emission changes from 2010, especially after the implementation of Clean Air Action in 2013, with significant air quality and human health benefits observed. Air pollutants, such as PM2.5 and surface ozone, as well as their precursors, have long enough lifetime in the troposphere which can be easily transported downwind. So emission changes in China will not only change the regional air quality domestically, but also affect the air quality in downwind regions. In this study, we use a global chemistry transport model to simulate the influence on both domestic and foreign air quality from the emission change from 2010 to 2017 in China. By applying the health impact functions derived from epidemiology studies, we then quantify the changes in air pollution-related (including both PM2.5 and O3) mortality burdens at regional and global scales. The majority of air pollutants in China reach their peak values around 2012 and 2013. Compared with the year 2010, the population-weighted annual PM2.5 in China increases till 2011 (94.1 μg m−3), and then begins to decrease. In 2017, the population-weighted annual PM2.5 decreases by 17.6 %, compared with the values in 2010 (84.7 μg m−3). The estimated national PM2.5 concentration changes in China are comparable with previous studies using fine-resolution regional models, though our model tends to overestimate PM2.5 from 2013 to 2017 when evaluated with surface observation in China during the same periods. The emission changes in China increased the global PM2.5-related mortality burdens from 2010 to 2013, by 27,700 (95 %CI: 23,900–31, 400) deaths yr−1 in 2011, and 13, 300 (11,400–15,100) deaths yr−1 in 2013, among which at least 93 % occurred in China. The sharp emission decreases after 2013 bring significant benefits for reduced avoided premature mortality in 2017, reaching 108, 800 (92,800–124,800) deaths yr−1 globally, among which 92 % happening in China. Different trend as PM2.5, the annual maximum daily 8-hr ozone in China increased, and also the ozone-related premature deaths, ranging from 3,600 (2,700–4,300) deaths yr−1 in 2011 (75 % of global total increased premature deaths), and 8,500 (6,500–9,900) deaths yr−1 in 2017 (143 % of the global total). Downwind regions, such as South Korea, Japan, and U.S. generally see a decreased O3-related mortality burden after 2013 as a combination of increased export of ozone and decreased export of ozone precursors. In general, we conclude that the sharp emission reductions in China after 2013 bring benefits of improved air quality and reduced premature deaths associated with air pollution at global scale. The benefits are dominated by the PM2.5 decreases since the ozone is shown to actually increase with the emission decrease.

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Fuzzy logic in air pollution: Revisited

Nepal Journal of Environmental Science ◽

10.3126/njes.v2i0.22735 ◽

2014 ◽

Vol 2 ◽

pp. 1-5

Author(s):

A. Deshpande

Keyword(s):

Air Pollution ◽

Fuzzy Logic ◽

Air Quality ◽

Epistemic Uncertainty ◽

Similarity Measures ◽

Epidemiological Studies ◽

Fuzzy Similarity ◽

Parametric Data ◽

Numeric Data ◽

Pollution Exposure

In everyday life and field, people mostly deal with concepts that involve factors that defy classification into crisp sets. The decisions people usually make are perceptions without rigorous analysis of numeric data. Like in other field of studies, there may exist imprecision in air quality parametric data collected and in the perception made by air quality experts in defining these parameters in linguistic terms such as: very good, good, poor. This is the reason why over the past few decades, soft computing tools such as fuzzy logic based methods, neural networks, and genetic algorithms have had significant and growing impacts to deal with aleatory as well as epistemic uncertainty in air quality related issues. This paper has highlighted mathematical preliminaries of air pollution studies like Similarity Measures (Cosine Amplitude Method), Fuzzy to Crisp Conversion (Alpha cut method), Fuzzy c Mean Clustering, Zadeh-Deshpande (ZD) Approach and linguistic description of air quality. Similarly, the applications of fuzzy similarity measures and fuzzy c mean clustering with defined possibility (- cut) levels in case air pollution studies for Delhi, India have been reflected. Though the approach of using fuzzy logic in pollution studies are not of common practice, the comprehensive approach that involves air pollution exposure surveys, toxicological data, and epidemiological studies coupled with fuzzy modeling will go a long way toward resolving some of the divisiveness and controversy in the current regulatory paradigm.

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Using a Network of Locally Developed Low Cost Particulate Matter Sensors for Land Use Regression Modeling of PM2.5 in Urban Uganda

10.20944/preprints202006.0158.v1 ◽

2020 ◽

Author(s):

Eric S. Coker ◽

Ssematimba Joel ◽

Engineer Bainomugisha

Keyword(s):

Land Use ◽

Air Pollution ◽

Particulate Matter ◽

Air Quality ◽

Low Cost ◽

Additive Models ◽

Pollution Monitoring ◽

Sub Saharan Africa ◽

Land Use Regression ◽

Sub Saharan

Background: There are major air pollution monitoring gaps in sub-Saharan Africa. Developing capacity in the region to conduct air monitoring in the region can help estimate exposure to air pollution for epidemiology research. The purpose of our study is to develop a land use regression (LUR) model using low-cost air quality sensors developed by a research group in Uganda (AirQo). Methods: Using these low-cost sensors, we collected continuous measurements of fine particulate matter (PM2.5) between May 1, 2019 and February 29, 2020 at 22 monitoring sites across urban municipalities of Uganda. We compared average monthly PM2.5 concentrations from the AirQo sensors with measurements from a BAM-1020 reference monitor operated at the US Embassy in Kampala. Monthly PM2.5 concentrations were used for LUR modeling. We used eight Machine Learning (ML) algorithms and ensemble modeling; using 10-fold cross validation and root mean squared error (RMSE) to evaluate model performance. Results: Monthly PM2.5 concentration was 60.2 µg/m3 (IQR: 45.4-73.0 µg/m3; median= 57.5 µg/m3). For the ML LUR models, RMSE values ranged between 5.43 µg/m3 - 15.43 µg/m3 and explained between 28% and 92% of monthly PM2.5 variability. Generalized additive models explained the largest amount of PM2.5 variability (R2=0.92) and produced the lowest RMSE (5.43 µg/m3) in the held-out test set. The most important predictors of monthly PM2.5 concentrations included monthly precipitation, major roadway density, population density, latitude, greenness, and percentage of households using solid fuels. Conclusion: To our knowledge, ours is the first study to model the spatial distribution of urban air pollution in sub-Saharan Africa using air monitors developed from the region itself. Non-parametric ML for LUR modeling performed with high accuracy for prediction of monthly PM2.5 levels. Our analysis suggests that locally produced low-cost air quality sensors can help build capacity to conduct air pollution epidemiology research in the region.

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Effect of the uncertainty in meteorology on air quality model predictions

Időjárás ◽

10.28974/idojaras.2021.4.5 ◽

2021 ◽

Vol 125 (4) ◽

pp. 625-646

Author(s):

Zita Ferenczi ◽

Emese Homolya ◽

Krisztina Lázár ◽

Anita Tóth

Keyword(s):

Air Pollution ◽

Particulate Matter ◽

Air Quality ◽

Weather Prediction ◽

Horizontal Resolution ◽

Model System ◽

Air Quality Model ◽

Quality Model ◽

Near Surface ◽

The Impact

An operational air quality forecasting model system has been developed and provides daily forecasts of ozone, nitrogen oxides, and particulate matter for the area of Hungary and three big cites of the country (Budapest, Miskolc, and Pécs). The core of the model system is the CHIMERE off-line chemical transport model. The AROME numerical weather prediction model provides the gridded meteorological inputs for the chemical model calculations. The horizontal resolution of the AROME meteorological fields is consistent with the CHIMERE horizontal resolution. The individual forecasted concentrations for the following 2 days are displayed on a public website of the Hungarian Meteorological Service. It is essential to have a quantitative understanding of the uncertainty in model output arising from uncertainties in the input meteorological fields. The main aim of this research is to probe the response of an air quality model to its uncertain meteorological inputs. Ensembles are one method to explore how uncertainty in meteorology affects air pollution concentrations. During the past decades, meteorological ensemble modeling has received extensive research and operational interest because of its ability to better characterize forecast uncertainty. One such ensemble forecast system is the one of the AROME model, which has an 11-member ensemble where each member is perturbed by initial and lateral boundary conditions. In this work we focus on wintertime particulate matter concentrations, since this pollutant is extremely sensitive to near-surface mixing processes. Selecting a number of extreme air pollution situations we will show what the impact of the meteorological uncertainty is on the simulated concentration fields using AROME ensemble members.

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Research Insights: What will People Pay for SMS Air Quality Alerts and Will They Avoid Air Pollution in Response?

10.18235/0003731 ◽

2021 ◽

Author(s):

Rema Hanna ◽

Bridget Hoffmann ◽

Paulina Oliva ◽

Jake Schneider

Keyword(s):

Air Pollution ◽

Particulate Matter ◽

Air Quality ◽

Willingness To Pay ◽

The Past ◽

High Pollution

Male, younger, and higher-income respondents as well as those who perceived high pollution in recent days showed greater willingness to pay for SMS air quality alerts. Willingness to pay was uncorrelated with actual recent high pollution. Recipients of SMS alerts indicated having received air pollution information via SMS, along with reporting a high-pollution day in the past week and having stayed indoors on the most recent day they perceived pollution to be high. However, alert recipients were not more accurate in identifying which specific days had high pollution than other respondents. Households that received a free N95 mask were more likely to report utilizing a mask with a filter during the past two weeks but not more likely to report using a mask with a filter on the specific days with high particulate matter.

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Air pollution and the development of asthma from birth until young adulthood

European Respiratory Journal ◽

10.1183/13993003.00147-2020 ◽

2020 ◽

Vol 56 (1) ◽

pp. 2000147 ◽

Cited By ~ 5

Author(s):

Ulrike Gehring ◽

Alet H. Wijga ◽

Gerard H. Koppelman ◽

Judith M. Vonk ◽

Henriette A. Smit ◽

...

Keyword(s):

Air Pollution ◽

Particulate Matter ◽

Early Adulthood ◽

Childhood And Adolescence ◽

Asthma Incidence ◽

Mite Allergy ◽

Increased Risk ◽

Air Pollution Exposure ◽

Pollution Exposure ◽

The Impact

BackgroundAir pollution is associated with asthma development in children and adults, but the impact on asthma development during the transition from adolescence to adulthood is unclear. Adult studies lack historical exposures and consequently cannot assess the relevance of exposure during different periods of life. We assessed the relevance of early-life and more recent air pollution exposure for asthma development from birth until early adulthood.MethodsWe used data of 3687 participants of the prospective Dutch PIAMA (Prevention and Incidence of Asthma and Mite Allergy) birth cohort and linked asthma incidence until age 20 years to estimated concentrations of nitrogen dioxide (NO2), particulate matter with a diameter <2.5 μm (PM2.5), <10 μm (PM10), and 2.5–10 μm, and PM2.5 absorbance (“soot”) at the residential address. We assessed overall and age-specific associations with air pollution exposure with discrete time-hazard models, adjusting for potential confounders.ResultsOverall, we found higher incidence of asthma until the age of 20 years with higher exposure to all pollutants at the birth address (adjusted odds ratio (95% CI) ranging from 1.09 (1.01–1.18) for PM10 to 1.20 (1.10–1.32) for NO2) per interquartile range increase) that were rather persistent with age. Similar associations were observed with more recent exposure defined as exposure at the current home address. In two-pollutant models with particulate matter, associations with NO2 persisted.ConclusionsExposure to air pollution, especially from motorised traffic, early in life may have long-term consequences for asthma development, as it is associated with an increased risk of developing asthma through childhood and adolescence into early adulthood.

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One-Year Simulation of Ozone and Particulate Matter in China Using WRF/CMAQ Modeling System

10.5194/acp-2016-148 ◽

2016 ◽

Cited By ~ 2

Author(s):

Jianlin Hu ◽

Jianjun Chen ◽

Qi Ying ◽

Hongliang Zhang

Keyword(s):

Air Pollution ◽

Particulate Matter ◽

Air Quality ◽

Model Performance ◽

Monitoring Network ◽

Organic Aerosol ◽

Quality Monitoring ◽

Air Quality Monitoring ◽

Low Concentration ◽

Temporal And Spatial

Abstract. China has been experiencing severe air pollution in recent decades. Although ambient air quality monitoring network for criteria pollutants has been constructed in over 100 cities since 2013 in China, the temporal and spatial characteristics of some important pollutants, such as particulate matter (PM) components, remain unknown, limiting further studies investigating potential air pollution control strategies to improve air quality and associating human health outcomes with air pollution exposure. In this study, a yearlong (2013) air quality simulation using the Weather Research & Forecasting model (WRF) and the Community Multi-scale Air Quality model (CMAQ) was conducted to provide detailed temporal and spatial information of ozone (O3), PM2.5 total and chemical components. Multi-resolution Emission Inventory for China (MEIC) was used for anthropogenic emissions and observation data obtained from the national air quality monitoring network were collected to validate model performance. The model successfully reproduces the O3 and PM2.5 concentrations at most cities for most months, with model performance statistics meeting the performance criteria. However, over-prediction of O3 generally occurs at low concentration range while under-prediction of PM2.5 happens at low concentration range in summer. Spatially, the model has better performance in Southern China than in Northern, Central and Sichuan basin. Strong seasonal variations of PM2.5 exist and wind speed and direction play important roles in high PM2.5 events. Secondary components have more boarder distribution than primary components. Sulfate (SO42−), nitrate (NO3−), ammonium (NH4+), and primary organic aerosol (POA) are the most important PM2.5 components. All components have the highest concentrations in winter except secondary organic aerosol (SOA). This study proves the ability of CMAQ model in reproducing severe air pollution in China, identifies the directions where improvements are needed, and provides information for human exposure to multiple pollutants for assessing health effects.

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