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

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.

scholarly journals The Power of Perception: Limitations of Information in Reducing Air Pollution Exposure

2021 ◽  
Author(s):  
Rema Hanna ◽  
Bridget Hoffmann ◽  
Paulina Oliva ◽  
Jake Schneider
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Avoidance Behavior ◽  
Controlled Trial ◽  
Randomized Controlled ◽  
The Past ◽  
Air Pollution Exposure ◽  
High Pollution ◽  
Pollution Exposure

We conduct a randomized controlled trial in Mexico City to determine willingness to pay (WTP) for SMS air quality alerts and to study the effects of air quality alerts, reminders, and a reusable N95 mask on air pollution information and avoidance behavior. At baseline, we elicit WTP for the alerts service after revealing whether the household will receive an N95 mask and participant compensation, but before revealing whether they will receive alert or reminder services. While we observe no significant impact of mask provision on WTP, higher compensation increases WTP, suggesting a possible cash-on-hand constraint. The perception of high pollution days prior to the survey is positively correlated with WTP, but the presence of actual high pollution days is not correlated with WTP. Follow-up survey data demonstrate that the alerts treatment increases reporting of receiving air pollution information via SMS, a high pollution day in the past week, and staying indoors on the most recent perceived high pollution day. However, we observe no significant effect on the ability to correctly identify which specific days had high pollution. Similarly, households that received an N95 mask are more likely to report utilizing a mask with filter in the past two weeks, but we observe no effect on using a filter mask on the specific days with high particulate matter. Although we nd that air quality alerts increased the salience of air quality and avoidance behavior, these results illustrate the difficulty that information treatments face in overcoming perceptions to effectively reduce exposure to air pollution.

scholarly journals The Estimation of the Willingness to Pay for Air-Quality Improvement in Thailand

2021 ◽  
Vol 13 (21) ◽  
pp. 12313
Author(s):  
Waranan Tantiwat ◽  
Christopher Gan ◽  
Wei Yang
Keyword(s):  
Air Pollution ◽  
Quality Improvement ◽  
Air Quality ◽  
Willingness To Pay ◽  
Contingent Valuation ◽  
Contingent Valuation Method ◽  
The Past ◽  
Valuation Method ◽  
Thai Government ◽  
Air Quality Improvement

Thailand has experienced severe air-quality problems for the past 10 years. Complicating this situation, the Thai government allocates an insufficient budget for the management of air pollution. Using the contingent valuation method, this paper estimates the willingness to pay for air-quality improvement in Thailand to reveal the benefits that people will gain if air-quality improves. The results show that the total benefits from air-quality improvement would be 18.8 billion baht in 2020. The Thai government can use these findings as a guideline to redistribute its budget to address air pollution more effectively.

Residents’ preferred measures and willingness-to-pay for improving urban air quality: A case study of Hanoi city, Vietnam

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Thanh Cong Nguyen ◽  
Hang Dieu Nguyen ◽  
Hoa Thu Le ◽  
Shinji Kaneko
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Willingness To Pay ◽  
Public Transportation ◽  
Online Survey ◽  
Urban Air Quality ◽  
Multivariate Probit ◽  
Urban Air ◽  
Content Type ◽  
Public Opinions

PurposeThis purpose of this paper is to understand residents’ choice of preferred measures and their willingness-to-pay (WTP) for the measures to improve the air quality of Hanoi city.Design/methodology/approachQuestionnaire surveys were conducted to collect the opinions of 212 household representatives living in Hanoi City. The survey tools were tested and adjusted through an online survey with 191 responses. Multivariate probit and linear regression models were used to identify determinants of respondents’ choices of measures and their WTP.FindingsRespondents expressed their strong preferences for three measures for air quality improvements, including: (1) increase of green spaces; (2) use of less polluting fuels; (3) expansion of public transportation. The mean WTP for the implementation of those measures was estimated at about 148,000–282,000 Vietnamese dong, equivalent to 0.09–0.16% of household income. The respondents’ choices appear to be consistent with their characteristics and needs, such as financial affordability, time on roads and their perceived impacts of air pollution. The WTP estimates increase with perception of air pollution impacts, time on roads, education and income; but are lower for older people.Originality/valueTo gain a better understanding of public opinions, we applied multivariate probit models to check whether respondents’ choices were consistent with their characteristics and perceptions. This appears to be the first attempt to test the validity of public opinions on choices of measures for improving urban air quality in Vietnam. Our WTP estimates also contribute to the database on the values of improved air quality in the developing world.

scholarly journals Particulate matter air pollution offsets ozone damage to global crop production

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.

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

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.

scholarly journals Effect of the uncertainty in meteorology on air quality model predictions

Időjárás ◽  
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.

scholarly journals Assessment of PM10 in Aurangabad City of Central India

2016 ◽  
Vol 5 (2) ◽  
pp. 61-74 ◽  
Author(s):  
Geetanjali Kaushik ◽  
Arvind Chel ◽  
Sangeeta Shinde ◽  
Ashish Gadekar
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Fine Particulate Matter ◽  
Tall Buildings ◽  
Central India ◽  
Air Pollutant ◽  
Traffic Density ◽  
High Pollution ◽  
High Concentrations ◽  
The City

Almost 670 million people comprising 54.5% of our population reside in regions that do not meet the Indian NAAQS for fine particulate matter. Numerous studies have revealed a consistent correlation for particulate matter concentration with health than any other air pollutant. Aurangabad city a rapidly growing city with population of 1.5 million is home to five major industrial areas, the city is also known for its historical monuments which might also be adversely affected from air pollution. Therefore, this research aims at estimating PM10 concentrations at several locations across Aurangabad. The concentration of PM10 was highest at the Railway Station followed by Waluj (an industrial zone) and City chowk is the centre of the city which has high population, tall buildings, few open spaces which causes high congestion and does not allow the particulates to disperse. Other locations with high concentrations of PM are Mill corner, Harsul T-point, Kranti Chowk, Seven Hill, TV centre and Beed Bye pass. All these locations have narrow roads, high traffic density, poor road condition with pot holes and few crossing points which cause congestion and vehicle idling which are responsible for high pollution. Therefore, it is evident that air pollution is a serious issue in the city which may be further aggravated if it is not brought under control. Hence, strategies have to be adopted for combating the menace of air pollution.INTERNATIONAL JOURNAL OF ENVIRONMENTVolume-5, Issue-2, March-May 2016, Page :61-74

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

2016 ◽  
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.

scholarly journals High Resolution Chemistry Transport Modeling with the On-Line CHIMERE-WRF Model over the French Alps—Analysis of a Feedback of Surface Particulate Matter Concentrations on Mountain Meteorology

Atmosphere ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 565 ◽  
Author(s):  
Bertrand Bessagnet ◽  
Laurent Menut ◽  
Rémy Lapere ◽  
Florian Couvidat ◽  
Jean-Luc Jaffrezo ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
High Altitude ◽  
Wrf Model ◽  
Synoptic Situation ◽  
Modeling Tools ◽  
Meteorological Model ◽  
French Alps ◽  
On Line

Air pollution is of major concern throughout the world and the use of modeling tools to analyze and forecast the pollutant concentrations in complex orographic areas remains challenging. This work proposes an exhaustive framework to analyze the ability of models to simulate the air quality over the French Alps up to 1.2 km resolution over Grenoble and the Arve Valley. The on-line coupled suite of models CHIMERE-WRF is used in its recent version to analyze a 1 month episode in November–December 2013. As expected, an improved resolution increases the concentrations close to the emission areas and reduced the negative bias for Particulate Matter that is the usual weakness of air quality models. However, the nitrate concentrations seem overestimated with at the same time an overestimation of surface temperature in the morning by WRF. Different WRF settings found in the literature are tested to improve the results, particularly the ability of the meteorological model to simulate the strong thermal inversions in the morning. Wood burning is one of the main contributor of air pollution during the period ranging from 80 to 90% of the Organic Matter. The activation of the on-line coupling has a moderate impact on the background concentrations but surprisingly a change of Particulate Matter (PM) concentrations in the valley will affect more the meteorology nearby high altitude areas than in the valley. This phenomenon is the result of a chain of processes involving the radiative effects and the water vapor column gradients in complex orographic areas. At last, the model confirms that the surrounding glaciers are largely impacted by long range transport of desert dust. However, in wintertime some outbreaks of anthropogenic pollution from the valley when the synoptic situation changes can be advected up to the nearby high altitude areas, then deposited.

scholarly journals The Impact of “Coal to Gas” Policy on Air Quality: Evidence from Beijing, China

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3876 ◽  
Author(s):  
Zhe Liu ◽  
Xueli Chen ◽  
Jinyang Cai ◽  
Tomas Baležentis ◽  
Yue Li
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Energy Use ◽  
Northern China ◽  
Heating System ◽  
Use Efficiency ◽  
The Government ◽  
The Impact ◽  
Beijing China

Air pollution has become an increasingly serious environmental problem in China. Especially in winter, the air pollution in northern China becomes even worse due to winter heating. The “coal to gas” policy, which uses natural gas to replace coal in the heating system in winter, was implemented in Beijing in the year 2013. However, the effects of this policy reform have not been examined. Using a panel dataset of 16 districts in Beijing, this paper employs a first difference model to examine the impact of the “coal to gas” policy on air quality. Strong evidence shows that the “coal to gas” policy has significantly improved the air quality in Beijing. On average, the “coal to gas” policy reduced sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter smaller than 10 µm (PM10), particulate matter smaller than 2.5 µm (PM2.5) and carbon monoxide (CO) by 12.08%, 4.89%, 13.07%, 11.94% and 11.10% per year, respectively. We find that the “coal to gas” policy is more effective in areas with less energy use efficiency. The finding of this paper suggests that the government should continue to implement the “coal to gas” policy, so as to alleviate the air pollution in Beijing, China.

Sign in / Sign up

Export Citation Format

Share Document