A four yearlong air monitoring data (since 2015 to 2018) was collected to assess the impact of meteorological parameters such as temperature, relative humidity, wind speed and wind direction on the spatial and temporal variability of CO (carbon monoxide), NO₂ (nitrogen oxide), O₃ (ozone), SO₂ (sulfur dioxide), PM₁₀ and PM_2.5 (particulate matter with diameter aerodynamic less than 2.5 μm and 10 μm respectively) in Lima Metropolitan Area (LMA). Data from eight air quality monitoring stations, currently under the supervision of the National Meteorological and Hydrological Service (SENAMHI), was used to capture traffic - related pollutants concentrations under various local traffic conditions. Effects on meteorological events on seasonal traffic-related pollutants concentration variability were identified. Using average data from the eight stations, it was found that the monthly mean traffic-related pollutants since 2015 to 2018 were higher in spring and winter and lower in autumn and summer. Low-wind speed was associated with events that contribute to elevated seasonal PM_2.5 levels. Correlation coefficients between PM_2.5 concentrations and meteorological variables fluctuated significaly across different seasons. High PM_2.5 concentrations were associated with low relative humidity and high wind conditions in spring. Results suggest that there exist a seasonal variation of PM_2.5 concentration, and set the need of future work on the understanding of the air pollution effects on human health.

Citizen and data science with the support of the Industrial Internet of Things (IIoT) present promising means to develop a dialogue between these themes air pollution, traffic assessment and new environmental commitment of the government for new laws, regulations, and other policy mechanisms associated with the stakeholders to improve outcomes for air quality management. In this research, a new proposal technology was implemented in Lima, Peru for air quality monitor sensors (AQMS). This solution has a new architecture for environmental analytics associated with a new assessment Spatio-temporal modeling of particulate elements PM 1 , PM2.5 and PM 10 ; humidity and the temperature associated with traffic.

Lima has been ranked among the top most polluted cities in the Americas. Vehicular emissions are the dominant source of pollution in the city. In order to reduce congestion and pollution levels during the XVIII Pan- and Parapan-American Games, Lima government officials enacted the pico y placa policy to restrict the number of vehicles on certain heavily trafficked roads in the city at rush hours between Monday to Thursday based on the last digit of their license plates. This policy was retained after the Games. In this paper we evaluate the impact of this policy on fine particulate matter concentration levels (PM_2.5) at a background site in the city using a difference-in-difference approach. We find that the policy resulted in increases on PM_2.5 levels on Monday-Thursday compared to Friday-Sunday levels after the policy was enacted, compared to previous years. However, such an increase was not significant. These results suggest the need for additional policies to reduce pollution due to traffic in Lima. It also suggests the need to track the response to this policy over time to evaluate its efficacy.