The Impact of the COVID-19 Emergency on Local Vehicular Traffic and Its Consequences for the Environment: The Case of the City of Reggio Emilia (Italy)

Role of vehicular emissions in urban air quality: The COVID-19 lockdown experiment

While the decrease in air pollutant concentration during the COVID-19 lockdown is well documented, neighborhood-scale and multi-city data have not yet been explored systematically to derive a generalizable quantitative link to the drop in vehicular traffic. To bridge this gap, high spatial resolution air quality and georeferenced traffic datasets were compiled for the city of London during three weeks with significant differences in traffic. The London analysis was then augmented with a meta-analysis of lower-resolution studies from 12 other cities. The results confirm that the improvement in air quality can be partially attributed to the drop of traffic density, and more importantly quantifies the elasticity (0.71 for NO₂ & 0.56 for PM_2.5) of their linkages. The findings can also inform on the future impacts of the ongoing shift to electric vehicles and micro-mobility on urban air quality.

Estimating road traffic impacts of commute mode shifts

This work considers the sensitivity of commute travel times in US metro areas due to potential changes in commute patterns, for example caused by events such as pandemics. Permanent shifts away from transit and carpooling can add vehicles to congested road networks, increasing travel times. Growth in the number of workers who avoid commuting and work from home instead can offset travel time increases. To estimate these potential impacts, 6-9 years of American Community Survey commute data for 118 metropolitan statistical areas are investigated. For 74 of the metro areas, the average commute travel time is shown to be explainable using only the number of passenger vehicles used for commuting. A universal Bureau of Public Roads model characterizes the sensitivity of each metro area with respect to additional vehicles. The resulting models are then used to determine the change in average travel time for each metro area in scenarios when 25% or 50% of transit and carpool users switch to single occupancy vehicles. Under a 25% mode shift, areas such as San Francisco and New York that are already congested and have high transit ridership may experience round trip travel time increases of 12 minutes (New York) to 20 minutes (San Francisco), costing individual commuters $1065 and $1601 annually in lost time. The travel time increases and corresponding costs can be avoided with an increase in working from home. The main contribution of this work is to provide a model to quantify the potential increase in commute travel times under various behavior changes, that can aid policy making for more efficient commuting.

Understanding travel behavior adjustment under COVID-19

The outbreak and spreading of the COVID-19 pandemic have had a significant impact on transportation system. By analyzing the impact of the pandemic on the transportation system, the impact of the pandemic on the social economy can be reflected to a certain extent, and the effect of anti-pandemic policy implementation can also be evaluated. In addition, the analysis results are expected to provide support for policy optimization. Currently, most of the relevant studies analyze the impact of the pandemic on the overall transportation system from the macro perspective, while few studies quantitatively analyze the impact of the pandemic on individual spatiotemporal travel behavior. Based on the license plate recognition (LPR) data, this paper analyzes the spatiotemporal travel patterns of travelers in each stage of the pandemic progress, quantifies the change of travelers' spatiotemporal behaviors, and analyzes the adjustment of travelers' behaviors under the influence of the pandemic. There are three different behavior adjustment strategies under the influence of the pandemic, and the behavior adjustment is related to the individual's past travel habits. The paper quantitatively assesses the impact of the COVID-19 pandemic on individual travel behavior. And the method proposed in this paper can be used to quantitatively assess the impact of any long-term emergency on individual micro travel behavior.

Short run "rebound effect" of COVID on the transport carbon footprint

The COVID-19 pandemic completely transformed the mobility of cities. The restrictions on movement led to "empty cities" throughout the world, with some environmental effects in terms of clean air and the reduction of CO₂ emissions. This research considers how COVID-19 mobility restrictions have affected the carbon footprint of four medium-sized Chilean cities (Coronel, Temuco, Valdivia, and Osorno) that have environmental problems and are highly dependent on motorized systems. The study uses data from 2400 household surveys at three distinct times: pre-pandemic - T0 (winter 2019), the time of implementation of restrictive mobility policies to contain the pandemic - T1 (winter 2020), and six months later when those restrictions were gradually lifted - T2 (summer 2021). The analysis suggests that CO₂ emissions actually went up, declining in the winter 2020, but then increasing with the greater use of cars in the summer 2021 due to the temporary effects of commuting to work, ultimately reaching levels higher than the pre-pandemic values, known as the "rebound effect."

How Positive and Negative Environmental Behaviours Influence Sustainable Tourism Intentions

This study developed and examined a theoretical model of moderated mediation in which positive and negative environmental behaviours (e.g., attitudes, destruction, conservation, and eco-friendliness) serve as a moderating mechanism that explains the link between the two critical mediating effects of escape and sustainable experiences on revisit intentions. The results of a study of 483 foreign tourists provide support for our hypothesized model. First, the results showed that motivations have indirect and positive effects on revisit intentions through sustainable experiences and escape-seeking. Second, the moderating effects of positive environmental behaviours were found to be positive, while negative environmental behaviours had negative effects on the dimensions of escape and experience on revisit intentions for sustainable tourism. Third, we discussed how this interesting pattern of the moderated mediation setting could be explained by using the theoretical background and considering previous studies on sustainable tourism.

ANN-Based traffic volume prediction models in response to COVID-19 imposed measures

Many countries around the globe have imposed several response measures to suppress the rapid spread of the COVID-19 pandemic since the beginning of 2020. These measures have impacted routine daily activities, along with their impact on economy, education, social and recreational activities, and domestic and international travels. Intuitively, the different imposed policies and measures have indirect impacts on urban traffic mobility. As a result of those imposed measures and policies, urban traffic flows have changed. However, those impacts are neither measured nor quantified. Therefore, estimating the impact of these combined yet different policies and measures on urban traffic flows is a challenging task. This paper demonstrates the development of an artificial neural networks (ANN) model which correlates the impact of the imposed response measure and other factors on urban traffic flows. The results show that the adopted ANN model is capable of mapping the complex relationship between traffic flows and the response measures with a high level of accuracy and good performance. The predicted values are closed to the observed ones. They are clustered around the regression line, with a coefficient of determination ( R 2 ) of 0.9761. Furthermore, the developed model can be generalized to determine the anticipated demand levels resulted from imposing any of the response measures in the post-pandemic era. This model can be used to manage traffic during mega-events. It can be also utilized for disaster or emergency situations, where traffic flow estimates are highly required for operational and planning purposes.

Transport impacts in Germany and State of Qatar: An assessment during the first wave of COVID-19

Response measures to contain COVID-19 spread varied from country to country, some imposed a complete lockdown while some imposed partial restrictions. This paper compares the transport impacts of the COVID-19 pandemic for two countries having dissimilar characteristics, Germany and State of Qatar, based on the rates of infection and response measures. Secondary data, obtained from Google mobility reports, and primary data, collected from local agencies, were used for comparison purposes. The secondary data comparison from February 2020 to July 2020 indicated an overall decline in mobility for all commercial activities and an increase was noted for parks and residential locations for Saxony, Germany. For State of Qatar, the mobility was decreased to all places except residential locations. Further, the comparison for traffic volumes and the number of crashes during the first wave of the pandemic indicated that the reduction in traffic volumes, major, and minor crashes was coupled with restrictive measures rather than COVID-19 incidences for both countries. Further, the traffic volumes showed a statistically significant inverse linear relationship with the stringency index for both countries during weekdays as well as weekends. These results suggest that the policy measures are key in governing movement restrictions and containing the spread of pandemic rather than the number of COVID-19 incidences. Further, the authorities should monitor the traffic trends during the pandemic and enforce the traffic rules and regulations as soon as the movement restrictions are lifted.

Air quality and photochemical reactions: analysis of NOx and NO2 concentrations in the urban area of Turin, Italy

In this work, based on the existing studies on photochemical reactions in the lower atmosphere, an analysis of the historical series of NO_x, NO₂, and O₃ concentrations measured in the period 2015-2019 by two monitoring stations located in the urban area of Turin, Italy, was elaborated. The objective was to investigate the concentration trends of the contaminants and evaluate possible simplified relationships based on the observed values. Concentration trends of these pollutants were compared in different time bands (diurnal or seasonal cycles), highlighting some differences in the dispersion of the validated data. Calculated [NO₂]/[NO_x] ratios were in agreement with the values observed in other urban areas worldwide. The influence of temperature on the [NO₂]/[NO_x] ratio was investigated. An increase of [NO₂]/[NO_x] concentration ratio was found with increasing temperature. Finally, a set of empirical relationships for the preliminary determination of NO₂ concentration values as a function of the NO_x was elaborated and compared with existing formulations. Polynomial functions were adapted to the average concentration values returned by the division into classes of 10 μg/m³ of NO_x. The choice of an empirical function to estimate the trend of NO₂ concentrations is potentially useful for the preliminary data analysis, especially in case of data scarcity. The scatter plots showed differences between the two monitoring stations, which may be attributable to a different urban context in which the stations are located. The dissonance between a purely residential context (Rubino station) and another characterised by the co-presence of residential buildings and industries of various kinds (Lingotto station) leads to the need to consider a greater contribution to the calculation of the concentrations emitted in an industrial/residential context due to a greater presence of industrial chimneys but also to more intense motorised vehicle transport. The analysis of the ratio between nitrogen oxides and tropospheric ozone confirmed that, as O₃ concentration increases, there is a consequent reduction of NO_x concentration, due to the chemical reactions of the photo-stationary cycle that takes place between the two species. This work highlighted that the use of an empirical formulation for the estimation of [NO_x] to [NO₂] conversion rate could in principle be adopted. However, the application of empirical models for the preliminary estimation of [NO_x] conversion to [NO₂] cannot replace advanced models and should be, in principle, restricted to a limited area and a limited range of NO_x concentrations.

Impact of COVID-19 Pandemic on Air Quality: A Systematic Review

With the emergence of the COVID-19 pandemic, several governments imposed severe restrictions on socio-economic activities, putting most of the world population into a general lockdown in March 2020. Although scattered, studies on this topic worldwide have rapidly emerged in the literature. Hence, this systematic review aimed to identify and discuss the scientifically validated literature that evaluated the impact of the COVID-19 pandemic and associated restrictions on air quality. Thus, a total of 114 studies that quantified the impact of the COVID-19 pandemic on air quality through monitoring were selected from three databases. The most evaluated countries were India and China; all the studies intended to evaluate the impact of the pandemic on air quality, mainly concerning PM₁₀, PM_2.5, NO₂, O₃, CO, and SO₂. Most of them focused on the 1st lockdown, comparing with the pre- and post-lockdown periods and usually in urban areas. Many studies conducted a descriptive analysis, while others complemented it with more advanced statistical analysis. Although using different methodologies, some studies reported a temporary air quality improvement during the lockdown. More studies are still needed, comparing different lockdown and lifting periods and, in other areas, for a definition of better-targeted policies to reduce air pollution.

Spatiotemporal Variation of Air Quality (PM and NO2) in Southern Paris during COVID-19 Lockdown Periods

In urban areas, road transportation is the main source of pollutants, and weather impacts spatiotemporal variation of air quality. In this paper, we demonstrate the spatiotemporal variabilities of particulate matter (PM10 and PM2.5) and NO2 in the south of Paris, with an emphasis on the comparison of air pollutant levels between COVID-19 lockdown and non-lockdown days according to meteorological conditions. The influence of COVID-19 lockdowns could be region-specific. Thus, it is worthwhile to conduct investigations across different regions and via different methodologies. This manuscript contains data that could be relied upon to evaluate available options for mitigation of urban air pollution. Using Airparif data and mobile survey data collected by Aeroqual 500 sensors, this study confirms that road traffic seems to be the determining factor of air quality in the southern part of Paris. The daily average concentrations of NO2, PM2.5 and PM10 calculated in 2020 show a strong spatiotemporal variability explained by the type of weather on the one hand and by the proximity of emission sources on the other hand. Mobile surveys show that during lockdowns in 2020, when the weather was stable, in 13th arrondissement of Paris, NO2 values exceeded 250 µg/m3 with PM10 values over 70 µg/m3, mainly in three locations: the area between Rue Tolbiac and Rue Nationale, along Rue de Chevaleret, and on Boulevard Périphérique.

Characterization of COVID-19's Impact on Mobility and Short-Term Prediction of Public Transport Demand in a Mid-Size City in Spain

COVID-19 has dramatically struck each section of our society: health, economy, employment, and mobility. This work presents a data-driven characterization of the impact of COVID-19 pandemic on public and private mobility in a mid-size city in Spain (Fuenlabrada). Our analysis used real data collected from the public transport smart card system and a Bluetooth traffic monitoring network, from February to September 2020, thus covering relevant phases of the pandemic. Our results show that, at the peak of the pandemic, public and private mobility dramatically decreased to 95% and 86% of their pre-COVID-19 values, after which the latter experienced a faster recovery. In addition, our analysis of daily patterns evidenced a clear change in the behavior of users towards mobility during the different phases of the pandemic. Based on these findings, we developed short-term predictors of future public transport demand to provide operators and mobility managers with accurate information to optimize their service and avoid crowded areas. Our prediction model achieved a high performance for pre- and post-state-of-alarm phases. Consequently, this work contributes to enlarging the knowledge about the impact of pandemic on mobility, providing a deep analysis about how it affected each transport mode in a mid-size city.

Evaluation of Air Quality Index by Spatial Analysis Depending on Vehicle Traffic during the COVID-19 Outbreak in Turkey

As in other countries of the world, the Turkish government is implementing many preventive partial and total lockdown practices against the virus’s infectious effect. When the first virus case has been detected, the public authorities have taken some restriction to reduce people and traffic mobility, which has also turned into some positive affect in air quality. To this end, the paper aims to examine how this pandemic affects traffic mobility and air quality in Istanbul. The pandemic does not only have a human health impact. This study also investigates the social and environmental effects. In our analysis, we observe, visualize, compare and discuss the impact of the post- and pre-lockdown on Istanbul’s traffic mobility and air quality. To do so, a geographic information system (GIS)-based approach is proposed. Various spatial analyses are performed in GIS with the statistical data used; thus, the environmental effects of the pandemic can be better observed. We test the hypothesis that this has reduced traffic mobility and improved air quality using traffic density cluster set and air monitoring stations (five air pollutant parameters) data for five months. The results shows that there are positive changes in terms of both traffic mobility and air quality, especially in April–May. PM10, SO2, CO, NO2 and NOx parameter values improved by 21.21%, 16.55%, 18.82%, 28.62% and 39.99%, respectively. In addition, there was a 7% increase in the average traffic speed. In order for the changes to be permanent, it is recommended to integrate e-mobility and sharing systems into the current transportation network.

Characterization of the vehicle emissions in the Greater Taipei Area through vision-based traffic analysis system and its impacts on urban air quality

In recent years, many surveillance cameras have been installed in the Greater Taipei Area, Taiwan; traffic data obtained from these surveillance cameras could be useful for the development of roadway-based emissions inventories. In this study, web-based traffic information covering the Greater Taipei Area was obtained using a vision-based traffic analysis system. Web-based traffic data were normalized and applied to the Community Multiscale Air Quality (CMAQ) model to study the impact of vehicle emissions on air quality in the Greater Taipei Area. According to an analysis of the obtained traffic data, sedans were the most common vehicles in the Greater Taipei Area, followed by motorcycles. Moderate traffic conditions with an average speed of 30-50 km/h were most prominent during weekdays, whereas traffic flow with an average speed of 50-70 km/h was most common during weekends. The proportion of traffic flows in free-flow conditions (>70 km/h) was higher on weekends than on weekdays. Two peaks of traffic flow were observed during the morning and afternoon peak hours on weekdays. On the weekends, this morning peak was not observed, and the variation in vehicle numbers was lower than on weekdays. The simulation results suggested that the addition of real-time traffic data improved the CMAQ model's performance, especially for the carbon monoxide (CO) and fine particulate matter (PM_2.5) concentrations. According to sensitivity tests for total and vehicle emissions in the Greater Taipei Area, vehicle emissions contributed to >90% of CO, 80% of nitrogen oxides (NO_x), and approximately 50% of PM_2.5 in the downtown areas of Taipei. The vehicle emissions contribution was affected by both vehicle emissions and meteorological conditions. The connection between the surveillance camera data, vehicle emissions, and regional air quality models in this study can also be used to explore the impact of special events (e.g., long weekends and COVID-19 lockdowns) on air quality.

How changes in human activities during the lockdown impacted air quality parameters: A review

The health emergency linked to the spread of COVID-19 has led to important reduction in industrial and logistics activities, as well as to a drastic changes in citizens' behaviors and habits. The restrictions on working activities, journeys and relationships imposed by the lockdown have had important consequences, including for environmental quality. This review aims to provide a structured and critical evaluation of the recent scientific bibliography that analyzed and described the impact of lockdown on human activities and on air quality. The results indicate an important effect of the lockdown during the first few months of 2020 on air pollution levels, compared to previous periods. The concentrations of particulate matter, nitrogen dioxide, sulfur dioxide and carbon monoxide have decreased. Tropospheric ozone, on the other hand, has significantly increased. These results are important indicators that can become decision drivers for future policies and strategies in industrial and logistics activities (including the mobility sector) aimed at their environmental sustainability. The scenario imposed by COVID-19 has supported the understanding of the link between the reduction of polluting emissions and the state of air quality and will be able to support strategic choices for the future sustainable growth of the industrial and logistics sector.

Air Pollution Observations in Selected Locations in Poland during the Lockdown Related to COVID-19

The coronavirus disease (COVID-19) has caused huge changes in people’s daily habits and had a significant impact on the economy. The lockdowns significantly reduced road traffic and meant that many people worked remotely. Therefore, the question arose as to how the reduced road traffic and stays of residents at home affected the degree of pollution and the structure of major air pollutants. To answer this question, the article presents an analysis of changes in typical air pollutants (PM10, PM2.5, NO2) in the five largest Polish cities and one of the voivodships. The data from the Polish State Environmental Monitoring were used for the analysis. The analysis showed that the period of the first lockdown in Poland (April 2020), despite the reduced road traffic, resulted in a significant increase in PM10 emissions (9–91% during working days and an average of 30% on Saturdays and Sundays), a slight increase in PM2.5 emissions (on average from 2% to 11% for all analyzed locations), and a reduction in NO2 emissions (on average from 6% to 11% for all analyzed locations) compared to the period before the lockdown. However, the changes were not homogeneous—in Łódź and Warsaw, in most cases, an increase in all analyzed pollutants was observed, and the greatest decrease in pollution took place in Małopolska voivodship (including Kraków). Comparing the data from April 2020 to the data from April 2019, the overall difference in the PMs concentrations was small, although there are places where there has been a significant decrease (Wrocław, Poznań), and there were also places where the concentration increased (Warsaw, Łódź, Małopolska). In the case of nitrogen dioxide, pollution concentration decreased in most locations. The only exception was the background stations in Warsaw, where the increase was 27%.

Quantifying Air Pollutant Variations during COVID-19 Lockdown in a Capital City in Northwest China

In the context of the outbreak of coronavirus disease 2019 (COVID-19), strict lockdown policies were implemented to control nonessential human activities in Xi’an, northwest China, which greatly limited the spread of the pandemic and affected air quality. Compared with pre-lockdown, the air quality index and concentrations of PM2.5, PM10, SO2, and CO during the lockdown reduced, but the reductions were not very significant. NO2 levels exhibited the largest decrease (52%) during lockdown, owing to the remarkable decreased motor vehicle emissions. The highest K+ and lowest Ca2+ concentrations in PM2.5 samples could be attributed to the increase in household biomass fuel consumption in suburbs and rural areas around Xi’an and the decrease in human physical activities in Xi’an (e.g., human travel, vehicle emissions, construction activities), respectively, during the lockdown period. Secondary chemical reactions in the atmosphere increased in the lockdown period, as evidenced by the increased O3 level (increased by 160%) and OC/EC ratios in PM2.5 (increased by 26%), compared with pre-lockdown levels. The results, based on a natural experiment in this study, can be used as a reference for studying the formation and source of air pollution in Xi’an and provide evidence for establishing future long-term air pollution control policies.

Travel Demand Prediction during COVID-19 Pandemic: Educational and Working Trips at the University of Padova

The diffusion of the COVID-19 pandemic has induced fundamental changes in travel habits. Although many previous authors have analysed factors affecting observed variations in travel demand, only a few works have focused on predictions of future new normal conditions when people will be allowed to decide whether to travel or not, although risk mitigation measures will still be enforced on vehicles, and innovative mobility services will be implemented. In addition, few authors have considered future mandatory trips of students that constitute a great part of everyday travels and are fundamental for the development of society. In this paper, logistic regression models were calibrated by using data from a revealed and stated-preferences mobility survey administered to students and employees at the University of Padova (Italy), to predict variables impacting on their decisions to perform educational and working trips in the new normal phase. Results highlighted that these factors are different between students and employees; furthermore, available travel alternatives and specific risk mitigation measures on vehicles were found to be significant. Moreover, the promotion of the use of bikes, as well as bike sharing, car pooling and micro mobility among students can effectively foster sustainable mobility habits. On the other hand, countermeasures on studying/working places resulted in a slight effect on travel decisions.

Evolution of Gaseous and Particulate Pollutants in the Air: What Changed after Five Lockdown Weeks at a Southwest Atlantic European Region (Northwest of Spain) Due to the SARS-CoV-2 Pandemic?

Due to the exponential growth of the SARS-CoV-2 pandemic in Spain (2020), the Spanish Government adopted lockdown measures as mitigating strategies to reduce the spread of the pandemic from 14 March. In this paper, we report the results of the change in air quality at two Atlantic Coastal European cities (Northwest Spain) during five lockdown weeks. The temporal evolution of gaseous (nitrogen oxides, comprising NOx, NO, and NO2; sulfur dioxide, SO2; carbon monoxide, CO; and ozone, O3) and particulate matter (PM10; PM2.5; and equivalent black carbon, eBC) pollutants were recorded before (7 February to 13 March 2020) and during the first five lockdown weeks (14 March to 20 April 2020) at seven air quality monitoring stations (urban background, traffic, and industrial) in the cities of A Coruña and Vigo. The influences of the backward trajectories and meteorological parameters on air pollutant concentrations were considered during the studied period. The temporal trends indicate that the concentrations of almost all species steadily decreased during the lockdown period with statistical significance, with respect to the pre-lockdown period. In this context, great reductions were observed for pollutants related mainly to fossil fuel combustion, road traffic, and shipping emissions (−38 to −78% for NO, −22 to −69% for NO2, −26 to −75% for NOx, −3 to −77% for SO2, −21% for CO, −25 to −49% for PM10, −10 to −38% for PM2.5, and −29 to −51% for eBC). Conversely, O3 concentrations increased from +5 to +16%. Finally, pollutant concentration data for 14 March to 20 April of 2020 were compared with those of the previous two years. The results show that the overall air pollutants levels were higher during 2018–2019 than during the lockdown period.

Changes in Air Quality Associated with Mobility Trends and Meteorological Conditions during COVID-19 Lockdown in Northern England, UK

The COVID-19 pandemic triggered catastrophic impacts on human life, but at the same time demonstrated positive impacts on air quality. In this study, the impact of COVID-19 lockdown interventions on five major air pollutants during the pre-lockdown, lockdown, and post-lockdown periods is analysed in three urban areas in Northern England: Leeds, Sheffield, and Manchester. A Generalised Additive Model (GAM) was implemented to eliminate the effects of meteorological factors from air quality to understand the variations in air pollutant levels exclusively caused by reductions in emissions. Comparison of lockdown with pre-lockdown period exhibited noticeable reductions in concentrations of NO (56.68–74.16%), NO2 (18.06–47.15%), and NOx (35.81–56.52%) for measured data. However, PM10 and PM2.5 levels demonstrated positive gain during lockdown ranging from 21.96–62.00% and 36.24–80.31%, respectively. Comparison of lockdown period with the equivalent period in 2019 also showed reductions in air pollutant concentrations, ranging 43.31–69.75% for NO, 41.52–62.99% for NOx, 37.13–55.54% for NO2, 2.36–19.02% for PM10, and 29.93–40.26% for PM2.5. Back trajectory analysis was performed to show the air mass origin during the pre-lockdown and lockdown periods. Further, the analysis showed a positive association of mobility data with gaseous pollutants and a negative correlation with particulate matter.

Framing Twitter Public Sentiment on Nigerian Government COVID-19 Palliatives Distribution Using Machine Learning

Sustainable development plays a vital role in information and communication technology. In times of pandemics such as COVID-19, vulnerable people need help to survive. This help includes the distribution of relief packages and materials by the government with the primary objective of lessening the economic and psychological effects on the citizens affected by disasters such as the COVID-19 pandemic. However, there has not been an efficient way to monitor public funds’ accountability and transparency, especially in developing countries such as Nigeria. The understanding of public emotions by the government on distributed palliatives is important as it would indicate the reach and impact of the distribution exercise. Although several studies on English emotion classification have been conducted, these studies are not portable to a wider inclusive Nigerian case. This is because Informal Nigerian English (Pidgin), which Nigerians widely speak, has quite a different vocabulary from Standard English, thus limiting the applicability of the emotion classification of Standard English machine learning models. An Informal Nigerian English (Pidgin English) emotions dataset is constructed, pre-processed, and annotated. The dataset is then used to classify five emotion classes (anger, sadness, joy, fear, and disgust) on the COVID-19 palliatives and relief aid distribution in Nigeria using standard machine learning (ML) algorithms. Six ML algorithms are used in this study, and a comparative analysis of their performance is conducted. The algorithms are Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM), Random Forest (RF), Logistics Regression (LR), K-Nearest Neighbor (KNN), and Decision Tree (DT). The conducted experiments reveal that Support Vector Machine outperforms the remaining classifiers with the highest accuracy of 88%. The “disgust” emotion class surpassed other emotion classes, i.e., sadness, joy, fear, and anger, with the highest number of counts from the classification conducted on the constructed dataset. Additionally, the conducted correlation analysis shows a significant relationship between the emotion classes of “Joy” and “Fear”, which implies that the public is excited about the palliatives’ distribution but afraid of inequality and transparency in the distribution process due to reasons such as corruption. Conclusively, the results from this experiment clearly show that the public emotions on COVID-19 support and relief aid packages’ distribution in Nigeria were not satisfactory, considering that the negative emotions from the public outnumbered the public happiness.