Particulate matter exposure from motorized traffic and risk of conversion from mild cognitive impairment to dementia: An Italian prospective cohort study.

BACKGROUND: Based on epidemiologic and laboratory studies, exposure to air pollutants has been linked to many adverse health effects including a higher risk of dementia. In this study, we aimed to evaluate the effect of long-term exposure to outdoor air pollution on risk of conversion to dementia in a cohort of subjects with mild cognitive impairment (MCI). METHODS: We recruited 53 Italian subjects newly-diagnosed with MCI. Within a geographical information system, we assessed recent outdoor air pollutant exposure, by modeling air levels of particulate matter with equivalent aerodynamic diameter ≤10 µm (PM10) from motorized traffic at participants' residence. We investigated the relation of PM10 concentrations to subsequent conversion from MCI to any type of dementia. Using a Cox-proportional hazards model combined with a restricted cubic spline model, we computed the hazard ratio (HR) of dementia with its 95% confidence interval (CI) according to increasing PM10 exposure, adjusting for sex, age, and educational attainment. RESULTS: During a median follow up of 47.3 months, 34 participants developed dementia, in 26 cases diagnosed as Alzheimer's dementia. In non-linear restricted spline regression analysis, mean and maximum annual PM10 levels positively correlated with cerebrospinal fluid total and phosphorylated tau proteins concentrations, while they were inversely associated with ß-amyloid. Concerning the risk of dementia, we found a positive association starting from above 10 µg/m3 for mean PM10 levels and above 35 µg/m3 for maximum PM10 levels. Specific estimates for Alzheimer's dementia were substantially similar. Adding other potential confounders to the multivariable model or removing early cases of dementia onset during the follow-up had little effect on the estimates. CONCLUSIONS: Our findings suggest that exposure to outdoor air pollutants, PM10 in particular, may non-linearly increase conversion from MCI to dementia above a certain ambient air concentration.

A deep learning approach for Spatio-Temporal forecasting of new cases and new hospital admissions of COVID-19 spread in Reggio Emilia, Northern Italy.

BACKGROUND: Since February 2020, the COVID-19 epidemic has rapidly spread throughout Italy. Some studies showed an association of environmental factors, such as PM10, PM2.5, NO2, temperature, relative humidity, wind speed, solar radiation and mobility with the spread of the epidemic. In this work, we aimed to predict via Deep Learning the real-time transmission of SARS-CoV-2 in the province of Reggio Emilia, Northern Italy, in a grid with a small resolution (12 km × 12 km), including satellite information. METHODS: We focused on the Province of Reggio Emilia, which was severely hit by the first wave of the epidemic. The outcomes included new SARS-CoV-2 infections and COVID-19 hospital admissions. Pollution, meteorological and mobility data were analyzed. The spatial simulation domain included the Province of Reggio Emilia in a grid of 40 cells of (12 km)2. We implemented a ConvLSTM, which is a spatio-temporal deep learning approach, to perform a 7-day moving average to forecast the 7th day after. We used as training and validation set the new daily infections and hospital admissions from August 2020 to March 2021. Finally, we assessed the models in terms of Mean Absolute Error (MAE) compared with Mean Observed Value (MOV) and Root Mean Squared Error (RMSE) on data from April to September 2021. We tested the performance of different combinations of input variables to find the best forecast model. FINDINGS: Daily new cases of infection, mobility and wind speed resulted in being strongly predictive of new COVID-19 hospital admissions (MAE = 2.72 in the Province of Reggio Emilia; MAE = 0.62 in Reggio Emilia city), whereas daily new cases, mobility, solar radiation and PM2.5 turned out to be the best predictors to forecast new infections, with appropriate time lags. INTERPRETATION: ConvLSTM achieved good performances in forecasting new SARS-CoV-2 infections and new COVID-19 hospital admissions. The spatio-temporal representation allows borrowing strength from data neighboring to forecast at the level of the square cell (12 km)2, getting accurate predictions also at the county level, which is paramount to help optimise the real-time allocation of health care resources during an epidemic emergency.

Risk of Amyotrophic Lateral Sclerosis and Exposure to Particulate Matter from Vehicular Traffic: A Case-Control Study.

(1) Background: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease with still unknown etiology. Some occupational and environmental risk factors have been suggested, including long-term air pollutant exposure. We carried out a pilot case-control study in order to evaluate ALS risk due to particulate matter with a diameter of ≤10 µm (PM10) as a proxy of vehicular traffic exposure. (2) Methods: We recruited ALS patients and controls referred to the Modena Neurology ALS Care Center between 1994 and 2015. Using a geographical information system, we modeled PM10 concentrations due to traffic emissions at the geocoded residence address at the date of case diagnosis. We computed the odds ratio (OR) and 95% confidence interval (CI) of ALS according to increasing PM10 exposure, using an unconditional logistic regression model adjusted for age and sex. (3) Results: For the 132 study participants (52 cases and 80 controls), the average of annual median and maximum PM10 concentrations were 5.2 and 38.6 µg/m3, respectively. Using fixed cutpoints at 5, 10, and 20 of the annual median PM10 levels, and compared with exposure <5 µg/m3, we found no excess ALS risk at 5-10 µg/m3 (OR 0.87, 95% CI 0.39-1.96), 10-20 µg/m3 (0.94, 95% CI 0.24-3.70), and ≥20 µg/m3 (0.87, 95% CI 0.05-15.01). Based on maximum PM10 concentrations, we found a statistically unstable excess ALS risk for subjects exposed at 10-20 µg/m3 (OR 4.27, 95% CI 0.69-26.51) compared with those exposed <10 µg/m3. However, risk decreased at 20-50 µg/m3 (OR 1.49, 95% CI 0.39-5.75) and ≥50 µg/m3 (1.16, 95% CI 0.28-4.82). ALS risk in increasing tertiles of exposure showed a similar null association, while comparison between the highest and the three lowest quartiles lumped together showed little evidence for an excess risk at PM10 concentrations (OR 1.13, 95% CI 0.50-2.55). After restricting the analysis to subjects with stable residence, we found substantially similar results. (4) Conclusions: In this pilot study, we found limited evidence of an increased ALS risk due to long-term exposure at high PM10 concentration, though the high statistical imprecision of the risk estimates, due to the small sample size, particularly in some exposure categories, limited our capacity to detect small increases in risk, and further larger studies are needed to assess this relation.

Associations between mortality from COVID-19 in two Italian regions and outdoor air pollution as assessed through tropospheric nitrogen dioxide.

After the appearance of COVID-19 in China last December 2019, Italy was the first European country to be severely affected by the outbreak. The first diagnosis in Italy was on February 20, 2020, followed by the establishment of a light and a tight lockdown on February 23 and on March 8, 2020, respectively. The virus spread rapidly, particularly in the North of the country in the 'Padan Plain' area, known as one of the most polluted regions in Europe. Air pollution has been recently hypothesized to enhance the clinical severity of SARS-CoV-2 infection, acting through adverse effects on immunity, induction of respiratory and other chronic disease, upregulation of viral receptor ACE-2, and possible pathogen transportation as a virus carrier. We investigated the association between air pollution and subsequent COVID-19 mortality rates within two Italian regions (Veneto and Emilia-Romagna). We estimated ground-level nitrogen dioxide through its tropospheric levels using data available from the Sentinel-5P satellites of the European Space Agency Copernicus Earth Observation Programme before the lockdown. We then examined COVID-19 mortality rates in relation to the nitrogen dioxide levels at three 14-day lag points after the lockdown, namely March 8, 22 and April 5, 2020. Using a multivariable negative binomial regression model, we found an association between nitrogen dioxide and COVID-19 mortality. Although ecological data provide only weak evidence, these findings indicate an association between air pollution levels and COVID-19 severity.

Satellite-detected tropospheric nitrogen dioxide and spread of SARS-CoV-2 infection in Northern Italy.

Following the outbreak of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) last December 2019 in China, Italy was the first European country to be severely affected, with the first local case diagnosed on 20 February 2020. The virus spread quickly, particularly in the North of Italy, with three regions (Lombardy, Veneto and Emilia-Romagna) being the most severely affected. These three regions accounted for >80% of SARS-CoV-2 positive cases when the tight lockdown was established (March 8). These regions include one of Europe's areas of heaviest air pollution, the Po valley. Air pollution has been recently proposed as a possible risk factor of SARS-CoV-2 infection, due to its adverse effect on immunity and to the possibility that polluted air may even carry the virus. We investigated the association between air pollution and subsequent spread of the SARS-CoV-2 infection within these regions. We collected NO2 tropospheric levels using satellite data available at the European Space Agency before the lockdown. Using a multivariable restricted cubic spline regression model, we compared NO2 levels with SARS-CoV-2 infection prevalence rate at different time points after the lockdown, namely March 8, 22 and April 5, in the 28 provinces of Lombardy, Veneto and Emilia-Romagna. We found little association of NO2 levels with SARS-CoV-2 prevalence up to about 130 µmol/m2, while a positive association was evident at higher levels at each time point. Notwithstanding the limitations of the use of aggregated data, these findings lend some support to the hypothesis that high levels of air pollution may favor the spread of the SARS-CoV-2 infection.

Lockdown timing and efficacy in controlling COVID-19 using mobile phone tracking.

BACKGROUND: Italy's severe COVID-19 outbreak was addressed by a lockdown that gradually increased in space, time and intensity. The effectiveness of the lockdown has not been precisely assessed with respect to the intensity of mobility restriction and the time until the outbreak receded. METHODS: We used processed mobile phone tracking data to measure mobility restriction, and related those data to the number of new SARS-CoV-2 positive cases detected on a daily base in the three most affected Italian regions, Lombardy, Veneto and Emilia-Romagna, from February 1 through April 6, 2020, when two subsequent lockdowns with increasing intensity were implemented by the Italian government. FINDINGS: During the study period, mobility restriction was inversely related to the daily number of newly diagnosed SARS-CoV-2 positive cases only after the second, more effective lockdown, with a peak in the curve of diagnosed cases of infection occurring 14 to 18 days from lockdown in the three regions and 9 to 25 days in the included provinces. An effective reduction in transmission must have occurred nearly immediately after the tighter lockdown, given the lag time of around 10 days from asymptomatic infection to diagnosis. The period from lockdown to peak was shorter in the areas with the highest prevalence of the infection. This effect was seen within slightly more than one week in the most severely affected areas. INTERPRETATION: It appears that the less rigid lockdown led to an insufficient decrease in mobility to reverse an outbreak such as COVID-19. With a tighter lockdown, mobility decreased enough to bring down transmission promptly below the level needed to sustain the epidemic. FUNDING: No funding sources have been used for this work.

Optimal air quality policies and health: a multi-objective nonlinear approach.

The use of modelling tools to support decision-makers to plan air quality policies is now quite widespread in Europe. In this paper, the Regional Integrated Assessment Tool (RIAT+), which was designed to support policy-maker decision on optimal emission reduction measures to improve air quality at minimum costs, is applied to the Porto Urban Area (Portugal). In addition to technological measures, some local measures were included in the optimization process. Case study results are presented for a multi-objective approach focused on both NO2 and PM10 control measures, assuming equivalent importance in the optimization process. The optimal set of air quality measures is capable to reduce simultaneously the annual average concentrations values of PM10 and NO2 in 1.7 and 1.0 µg/m3, respectively. This paper illustrates how the tool could be used to prioritize policy objectives and help making informed decisions about reducing air pollution and improving public health.

Determinants of serum manganese levels in an Italian population.

Manganese (Mn) is both essential and toxic for humans, mainly depending on the total levels and its species. Main sources of exposure include food and air pollution, particularly motorized traffic. We sought to determine the potential influence of these sources on serum total levels of Mn and Mn species. We selected a random sample of municipality residents from an Italian urban municipality, from whom we collected detailed personal information, dietary habits and a blood sample for serum Mn determination. We also assessed outdoor air Mn exposure, by modeling levels of particulate matter ≤10 µm (PM10) from motorized traffic at the residence of geocoded subjects. Serum Mn species generally showed higher levels in males and positive correlation with age, while no such differences were found according to smoking habits or use of dietary supplements. Among nutrients, only iron intake showed a relation with Mn [an inverse correlation with Mn­ferritin (Mn­Fer) and a direct one with inorganic­Mn (Inorg­Mn)]. Meat consumption directly correlated and fish and seafood inversely correlated with total Mn, Mn­transferrin (Mn­Tf) and Mn-citrate (Mn-Cit). Fruits and vegetables, including legumes and nuts, generally showed a positive correlation with all Mn species, especially Mn­Cit, and an inverse one with Inorg­Mn. Odds ratios (ORs) of having serum Mn levels above median value increased with increasing PM10 tertiles, with an OR for highest­to­lowest tertile of 7.40 (1.36­40.25) in multivariate analysis. Analyses for Mn species did not highlight a clear comparable pattern. In conclusion, our results seem to demonstrate that PM10 exposure positively influences total Mn serum levels, while single Mn species show conflicting results.

Determinants of serum cadmium levels in a Northern Italy community: A cross-sectional study.

INTRODUCTION: Cadmium (Cd) is a heavy metal and a serious environmental hazard to humans. Some uncertainties still exist about major sources of Cd exposure in non-occupationally exposed subjects in addition to cigarette smoking, such as diet and outdoor air pollution. We sought to determine the influence of these sources on a biomarker of exposure, serum Cd concentration. METHODS: We recruited 51 randomly selected residents from an Italian urban community, from whom we obtained detailed information about dietary habits and smoking habits, and a blood sample for serum Cd determination. We also assessed outdoor air Cd exposure, by modeling outdoor air levels of particulate matter ≤10µm (PM10) from motorized traffic at geocoded subjects' residence. RESULTS: In crude analysis, regression beta coefficients for dietary Cd, smoking and PM10 on serum Cd levels were 0.03 (95% CI -0.83 to 0.88), 6.96 (95% CI -0.02 to 13.95) and 0.62 (95% CI -0.19 to 1.43), respectively. In the adjusted analysis, regression beta coefficients were -0.34 (95% CI -1-40 to 0.71), 5.81 (95% CI -1.43 to 13.04) and 0.47 (95% CI -0.35 to 1.29), respectively. CONCLUSION: Cigarette smoking was the most important factor influencing serum Cd in our non-occupationally exposed population, as expected, while dietary Cd was not associated with this biomarker. Outdoor air pollution, as assessed through exposure to particulate matter generated by motorized traffic, was an additional source of Cd exposure.

Does maternal exposure to benzene and PM10 during pregnancy increase the risk of congenital anomalies? A population-based case-control study.

A few studies have suggested an association between maternal exposure to ambient air pollution from vehicular traffic and risk of congenital anomalies in the offspring, but epidemiologic evidence is neither strong nor entirely consistent. In a population-based case-control study in a Northern Italy community encompassing 228 cases of birth defects and 228 referent newborns, we investigated if maternal exposure to PM10 and benzene from vehicular traffic during early pregnancy, as estimated through a dispersion model, was associated with excess teratogenic risk. In conditional logistic regression analysis, and with adjustment for the other pollutant, we found that higher exposure to PM10 but not benzene was associated with increased risk of birth defects overall. Anomaly categories showing the strongest dose-response relation with PM10 exposure were musculoskeletal and chromosomal abnormalities but not cardiovascular defects, with Down syndrome being among the specific abnormalities showing the strongest association, though risk estimates particularly for the less frequent defects were statistically very unstable. Further adjustment in the regression model for potential confounders did not considerably alter the results. All the associations were stronger for average levels of PM10 than for their maximal level. Findings of this study give some support for an excess teratogenic risk following maternal exposure during pregnancy to PM10, but not benzene. Such association appears to be limited to some birth defect categories.

Exploring trade-offs between air pollutants through an Integrated Assessment Model.

When designing air pollution reduction policies, regional decision makers face a limited budget to choose the most efficient measures which will have impacts on several pollutants in different ways. RIAT+ is a regional integrated assessment tool that supports the policy maker in this selection of the optimal emission reduction technologies, to improve air quality at minimum costs. In this paper, this tool is formalized and applied to the specific case of a French region (Alsace), to illustrate how focusing on one single pollutant may exacerbate problems related to other pollutants, on top of conflicts related to budget allocation. In our case, results are shown for possible trade-offs between NO2 and O3 control policies. The paper suggests an approach to prioritize policy maker objectives when planning air pollution policies at regional scale.

Leukemia risk in children exposed to benzene and PM10 from vehicular traffic: a case-control study in an Italian population.

Benzene, a recognized occupational leukemogen in adults, has been hypothesized to also increase the risk of childhood leukemia. We carried out a population-based case-control study in a northern Italy community involving 83 cases with acute childhood leukemia diagnosed in the years 1998-2009 and 332 matched controls. We assessed residential exposure to benzene and to particulate matter ≤10 µm (PM10) from motorized traffic using geocoded residences and detailed emission and dispersion modeling. Exposure to benzene, and to a lesser extent to PM10, appeared to be independently associated with an excess leukemia risk. When we stratified the study population by age and by leukemia subtype, the relative risk associated with benzene exposure was higher among children aged less than 5 years, and despite small numbers this relation appeared to be considerably stronger for acute myeloid leukemia than for acute lymphoblastic leukemia. Overall, these findings suggest that exposure to low levels of benzene released from motorized traffic may increase the risk of childhood leukemia, and suggest a possible independent effect of PM10, although unmeasured confounding due to other pollutants cannot be ruled out.

Methods for comparing gridded inventories of atmospheric emissions--application for Milan province, Italy and the Greater Athens Area, Greece.

A set of methods has been compiled to compare gridded air emission inventories that have been derived, on the same spatial grid, using widely differing techniques. Top-down and bottom-up inventories for Milan, Italy and for the Greater Athens area (GAA), Greece were used to test and apply these methods. The applicability of each method to certain source sectors was assessed by conducting sensitivity analyses. Whilst some of the methods (such as regression calculations or the Moran coefficient) appeared very sensitive to variations of point source emissions, others (e.g. the construction of difference maps) proved more appropriate for characterizing line source differences. Area sources could best be dealt with by using the newly developed acceptability criterion. The development of these tools allows a reproducible comparison of sets of emission inventories and consequently supports developments towards improvement.